{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "QUANTAXIS>> start QUANTAXIS\n",
      "QUANTAXIS>> Welcome to QUANTAXIS, the Version is 1.0.46\n",
      "QUANTAXIS>>  \n",
      " ```````````````````````````````````````````````````````````````````````````````````````````````````````````````````````` \n",
      "  ``########`````##````````##``````````##`````````####````````##```##########````````#``````##``````###```##`````######`` \n",
      "  `##``````## ```##````````##`````````####````````##`##```````##```````##```````````###``````##````##`````##```##`````##` \n",
      "  ##````````##```##````````##````````##`##````````##``##``````##```````##``````````####```````#```##``````##```##``````## \n",
      "  ##````````##```##````````##```````##```##```````##```##`````##```````##`````````##`##```````##`##```````##````##``````` \n",
      "  ##````````##```##````````##``````##`````##``````##````##````##```````##````````##``###```````###````````##`````##`````` \n",
      "  ##````````##```##````````##``````##``````##`````##`````##```##```````##```````##````##```````###````````##``````###```` \n",
      "  ##````````##```##````````##`````##````````##````##``````##``##```````##``````##``````##`````##`##```````##````````##``` \n",
      "  ##````````##```##````````##````#############````##```````##`##```````##`````###########`````##``##``````##`````````##`` \n",
      "  ###```````##```##````````##```##```````````##```##```````##`##```````##````##`````````##```##```##``````##```##`````##` \n",
      "  `##``````###````##``````###``##`````````````##``##````````####```````##```##``````````##``###````##`````##````##`````## \n",
      "  ``#########``````########```##``````````````###`##``````````##```````##``##````````````##`##``````##````##`````###``### \n",
      "  ````````#####`````````````````````````````````````````````````````````````````````````````````````````````````````##`` \n",
      "  ``````````````````````````````````````````````````````````````````````````````````````````````````````````````````````` \n",
      "  ``````````````````````````Copyright``yutiansut``2018``````QUANTITATIVE FINANCIAL FRAMEWORK````````````````````````````` \n",
      "  ``````````````````````````````````````````````````````````````````````````````````````````````````````````````````````` \n",
      " ```````````````````````````````````````````````````````````````````````````````````````````````````````````````````````` \n",
      " ```````````````````````````````````````````````````````````````````````````````````````````````````````````````````````` \n",
      " \n"
     ]
    }
   ],
   "source": [
    "import QUANTAXIS as QA\n",
    "try:\n",
    "    assert QA.__version__>='1.0.46'\n",
    "except AssertionError:\n",
    "    print('pip install QUANTAXIS >= 1.0.46 请升级QUANTAXIS后再运行此示例')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "首先确定你已经完成了对于QUANTAXIS的基础认知,以及在本地存储完毕了QUANTAXIS的数据库\n"
     ]
    }
   ],
   "source": [
    "print('首先确定你已经完成了对于QUANTAXIS的基础认知,以及在本地存储完毕了QUANTAXIS的数据库')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QUANTAXIS 回测的一些基础知识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  QA回测的核心是两个类\n",
    "\n",
    "```\n",
    "QA_BacktestBroker\n",
    "QA_Account\n",
    "```\n",
    "\n",
    "##  回测数据的引入/迭代\n",
    "\n",
    "```\n",
    "QA.QA_fetch_stock_day_adv\n",
    "QA.QA_fetch_stock_min_adv\n",
    "```\n",
    "\n",
    "##  指标的计算\n",
    "\n",
    "```\n",
    "DataStruct.add_func\n",
    "```\n",
    "\n",
    "##  对于账户的灵活运用\n",
    "\n",
    "```\n",
    "QA_Account\n",
    "QA_Risk\n",
    "QA_Portfolio\n",
    "QA_PortfolioView\n",
    "QA_User\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP1 初始化账户,初始化回测broker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: QUANTAXIS 1.0.47 has changed the init_assets ==> init_cash, please pay attention to this change if you using init_cash to initial an account class,                \n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Account=QA.QA_Account()\n",
    "Broker=QA.QA_BacktestBroker()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'account_cookie': 'Acc_9hlQfsua',\n",
      " 'allow_sellopen': False,\n",
      " 'allow_t0': False,\n",
      " 'broker': 'backtest',\n",
      " 'cash': [1000000],\n",
      " 'commission_coeff': 0.00025,\n",
      " 'current_time': None,\n",
      " 'history': [],\n",
      " 'init_assets': {'cash': 1000000, 'hold': {}},\n",
      " 'margin_level': False,\n",
      " 'market_type': 'stock_cn',\n",
      " 'portfolio_cookie': None,\n",
      " 'quantaxis_version': '1.0.46',\n",
      " 'running_environment': 'backtest',\n",
      " 'running_time': datetime.datetime(2018, 6, 11, 23, 45, 7, 571001),\n",
      " 'source': 'account',\n",
      " 'strategy_name': None,\n",
      " 'tax_coeff': 0.0015,\n",
      " 'trade_index': [],\n",
      " 'user_cookie': None}\n"
     ]
    }
   ],
   "source": [
    "# 打印账户的信息\n",
    "try:\n",
    "    from pprint import  pprint as print\n",
    "except:\n",
    "    pass\n",
    "print(Account.message)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 首先讲解Account类:\n",
    "\n",
    "QA_Account在初始化的时候,可以自己指定很多信息:\n",
    "\n",
    "```\n",
    "\n",
    "    QA_Account(\n",
    "        strategy_name=None, user_cookie=None, portfolio_cookie=None, account_cookie=None,\n",
    "        market_type=MARKET_TYPE.STOCK_CN, frequence=FREQUENCE.DAY, broker=BROKER_TYPE.BACKETEST,\n",
    "        init_hold={}, init_cash=1000000, commission_coeff=0.00025, tax_coeff=0.0015,\n",
    "        margin_level=False, allow_t0=False, allow_sellopen=False,\n",
    "        running_environment=RUNNING_ENVIRONMENT.BACKETEST)\n",
    "\n",
    "        :param [str] strategy_name:  策略名称\n",
    "        :param [str] user_cookie:   用户cookie\n",
    "        :param [str] portfolio_cookie: 组合cookie\n",
    "        :param [str] account_cookie:   账户cookie\n",
    "\n",
    "        :param [dict] init_hold         初始化时的股票资产\n",
    "        :param [float] init_cash:         初始化资金\n",
    "        :param [float] commission_coeff:  交易佣金 :默认 万2.5   float 类型\n",
    "        :param [float] tax_coeff:         印花税   :默认 千1.5   float 类型\n",
    "\n",
    "        :param [Bool] margin_level:      保证金比例 默认False\n",
    "        :param [Bool] allow_t0:          是否允许t+0交易  默认False\n",
    "        :param [Bool] allow_sellopen:    是否允许卖空开仓  默认False\n",
    "\n",
    "        :param [QA.PARAM] market_type:   市场类别 默认QA.MARKET_TYPE.STOCK_CN A股股票\n",
    "        :param [QA.PARAM] frequence:     账户级别 默认日线QA.FREQUENCE.DAY\n",
    "        :param [QA.PARAM] broker:        BROEKR类 默认回测 QA.BROKER_TYPE.BACKTEST\n",
    "        :param [QA.PARAM] running_environment 当前运行环境 默认Backtest\n",
    "\n",
    "        # 2018/06/11 init_assets 从float变为dict,并且不作为输入,作为只读属性\n",
    "        #  :param [float] init_assets:       初始资产  默认 1000000 元 （100万）\n",
    "        init_assets:{\n",
    "            cash: xxx,\n",
    "            stock: {'000001':2000},\n",
    "            init_date: '2018-02-05',\n",
    "            init_datetime: '2018-02-05 15:00:00'\n",
    "        }\n",
    "        # 2018/06/11 取消在初始化的时候的cash和history输入\n",
    "        # :param [list] cash:              可用现金  默认 是 初始资产  list 类型\n",
    "        # :param [list] history:           交易历史\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重设账户初始资金\n",
    "\n",
    "Account.reset_assets(200000)\n",
    "Account.account_cookie='JCSC_EXAMPLE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cash': 200000, 'hold': {}}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.init_assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Account 有很多方法,暂时不详细展开,我们先直接进入下一步"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SETP2:引入回测的市场数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "引入方法非常简单,直接使用QA_fetch_stock_day_adv系列即可\n",
    "\n",
    "- code 可以是多种多样的选取方式\n",
    "\n",
    "```python\n",
    "1. QA.QA_fetch_stock_list_adv().code.tolist() # 获取全市场的股票代码\n",
    "2. QA.QA_fetch_stock_block_adv().get_block('云计算').code  # 按版块选取\n",
    "3. code= ['000001','000002'] # 自己指定\n",
    "```\n",
    "- 数据获取后,to_qfq() 即可获得前复权数据\n",
    "\n",
    "```python\n",
    "data=DataSturct.to_qfq()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# QA.QA_fetch_stock_list_adv().code.tolist()\n",
    "# QA.QA_fetch_stock_block_adv().get_block('云计算').code\n",
    "codelist=QA.QA_fetch_stock_block_adv().get_block('云计算').code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=QA.QA_fetch_stock_day_adv(codelist,'2017-09-01','2018-05-20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "< QA_DataStruct_Stock_day with 65 securities >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\quantaxis\\QUANTAXIS\\QAData\\QADataStruct.py:114: FutureWarning: 'code' is both an index level and a column label.\n",
      "Defaulting to column, but this will raise an ambiguity error in a future version\n",
      "  self.data.groupby('code').apply(QA_data_stock_to_fq), self.type, 'qfq')\n"
     ]
    }
   ],
   "source": [
    "data=data.to_qfq()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP3:计算一些指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "指标的计算可以在回测前,也可以在回测中进行\n",
    "\n",
    "回测前的计算则是批量计算,效率较高\n",
    "\n",
    "回测中的计算,效率略低,但代码量较小,易于理解\n",
    "\n",
    "PS: 指标的相关介绍参见 [QUANTAXIS的指标系统](https://github.com/QUANTAXIS/QUANTAXIS/blob/master/Documents/indicators.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "def MACD_JCSC(dataframe,SHORT=12,LONG=26,M=9):\n",
    "    \"\"\"\n",
    "    1.DIF向上突破DEA，买入信号参考。\n",
    "    2.DIF向下跌破DEA，卖出信号参考。\n",
    "    \"\"\"\n",
    "    CLOSE=dataframe.close\n",
    "    DIFF =QA.EMA(CLOSE,SHORT) - QA.EMA(CLOSE,LONG)\n",
    "    DEA = QA.EMA(DIFF,M)\n",
    "    MACD =2*(DIFF-DEA)\n",
    "\n",
    "    CROSS_JC=QA.CROSS(DIFF,DEA)\n",
    "    CROSS_SC=QA.CROSS(DEA,DIFF)\n",
    "    ZERO=0\n",
    "    return pd.DataFrame({'DIFF':DIFF,'DEA':DEA,'MACD':MACD,'CROSS_JC':CROSS_JC,'CROSS_SC':CROSS_SC,'ZERO':ZERO})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind=data.add_func(MACD_JCSC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d32c989860>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind.xs(codelist[0],level=1)['2018-01'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DIFF</th>\n",
       "      <th>DEA</th>\n",
       "      <th>MACD</th>\n",
       "      <th>CROSS_JC</th>\n",
       "      <th>CROSS_SC</th>\n",
       "      <th>ZERO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.062832</td>\n",
       "      <td>0.034908</td>\n",
       "      <td>0.055848</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.044543</td>\n",
       "      <td>0.077076</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.096405</td>\n",
       "      <td>0.054915</td>\n",
       "      <td>0.082979</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.101761</td>\n",
       "      <td>0.064284</td>\n",
       "      <td>0.074953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>0.096831</td>\n",
       "      <td>0.070794</td>\n",
       "      <td>0.052074</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>0.087880</td>\n",
       "      <td>0.074211</td>\n",
       "      <td>0.027338</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>0.127678</td>\n",
       "      <td>0.084904</td>\n",
       "      <td>0.085548</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>0.239488</td>\n",
       "      <td>0.115821</td>\n",
       "      <td>0.247333</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>0.315590</td>\n",
       "      <td>0.155775</td>\n",
       "      <td>0.319631</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-15</th>\n",
       "      <td>0.413861</td>\n",
       "      <td>0.207392</td>\n",
       "      <td>0.412937</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>0.435121</td>\n",
       "      <td>0.252938</td>\n",
       "      <td>0.364367</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>0.358336</td>\n",
       "      <td>0.274018</td>\n",
       "      <td>0.168637</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>0.281338</td>\n",
       "      <td>0.275482</td>\n",
       "      <td>0.011713</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>0.220198</td>\n",
       "      <td>0.264425</td>\n",
       "      <td>-0.088454</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>0.158625</td>\n",
       "      <td>0.243265</td>\n",
       "      <td>-0.169280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>0.099806</td>\n",
       "      <td>0.214573</td>\n",
       "      <td>-0.229534</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>0.070925</td>\n",
       "      <td>0.185844</td>\n",
       "      <td>-0.229837</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>0.041907</td>\n",
       "      <td>0.157056</td>\n",
       "      <td>-0.230298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>0.027466</td>\n",
       "      <td>0.131138</td>\n",
       "      <td>-0.207344</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>-0.001704</td>\n",
       "      <td>0.104570</td>\n",
       "      <td>-0.212547</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>-0.053246</td>\n",
       "      <td>0.073007</td>\n",
       "      <td>-0.252505</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>-0.121729</td>\n",
       "      <td>0.034059</td>\n",
       "      <td>-0.311578</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                DIFF       DEA      MACD  CROSS_JC  CROSS_SC  ZERO\n",
       "date                                                              \n",
       "2018-01-02  0.062832  0.034908  0.055848         0         0     0\n",
       "2018-01-03  0.083081  0.044543  0.077076         0         0     0\n",
       "2018-01-04  0.096405  0.054915  0.082979         0         0     0\n",
       "2018-01-05  0.101761  0.064284  0.074953         0         0     0\n",
       "2018-01-08  0.096831  0.070794  0.052074         0         0     0\n",
       "2018-01-09  0.087880  0.074211  0.027338         0         0     0\n",
       "2018-01-10  0.127678  0.084904  0.085548         0         0     0\n",
       "2018-01-11  0.239488  0.115821  0.247333         0         0     0\n",
       "2018-01-12  0.315590  0.155775  0.319631         0         0     0\n",
       "2018-01-15  0.413861  0.207392  0.412937         0         0     0\n",
       "2018-01-16  0.435121  0.252938  0.364367         0         0     0\n",
       "2018-01-17  0.358336  0.274018  0.168637         0         0     0\n",
       "2018-01-18  0.281338  0.275482  0.011713         0         0     0\n",
       "2018-01-19  0.220198  0.264425 -0.088454         0         1     0\n",
       "2018-01-22  0.158625  0.243265 -0.169280         0         0     0\n",
       "2018-01-23  0.099806  0.214573 -0.229534         0         0     0\n",
       "2018-01-24  0.070925  0.185844 -0.229837         0         0     0\n",
       "2018-01-25  0.041907  0.157056 -0.230298         0         0     0\n",
       "2018-01-26  0.027466  0.131138 -0.207344         0         0     0\n",
       "2018-01-29 -0.001704  0.104570 -0.212547         0         0     0\n",
       "2018-01-30 -0.053246  0.073007 -0.252505         0         0     0\n",
       "2018-01-31 -0.121729  0.034059 -0.311578         0         0     0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind.xs(codelist[0],level=1)['2018-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DIFF</th>\n",
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       "      <th rowspan=\"30\" valign=\"top\">2018-01-02</th>\n",
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       "      <td>-0.130608</td>\n",
       "      <td>0.018681</td>\n",
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       "      <th>300017</th>\n",
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       "      <td>0.236589</td>\n",
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       "      <td>-0.493753</td>\n",
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       "      <td>0.025245</td>\n",
       "      <td>-0.340159</td>\n",
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       "      <td>-0.121110</td>\n",
       "      <td>-0.046036</td>\n",
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       "    <tr>\n",
       "      <th>300290</th>\n",
       "      <td>-0.154023</td>\n",
       "      <td>-0.166551</td>\n",
       "      <td>0.025057</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>300297</th>\n",
       "      <td>-0.146436</td>\n",
       "      <td>-0.178916</td>\n",
       "      <td>0.064962</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2018-01-31</th>\n",
       "      <th>300366</th>\n",
       "      <td>-0.195730</td>\n",
       "      <td>-0.074191</td>\n",
       "      <td>-0.243077</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>300379</th>\n",
       "      <td>-0.184565</td>\n",
       "      <td>-0.186722</td>\n",
       "      <td>0.004316</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>300608</th>\n",
       "      <td>-0.347437</td>\n",
       "      <td>-0.370488</td>\n",
       "      <td>0.046103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>600037</th>\n",
       "      <td>-0.050774</td>\n",
       "      <td>-0.090620</td>\n",
       "      <td>0.079691</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>600100</th>\n",
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       "      <th>600410</th>\n",
       "      <td>-0.088613</td>\n",
       "      <td>-0.075791</td>\n",
       "      <td>-0.025643</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600522</th>\n",
       "      <td>-0.557657</td>\n",
       "      <td>-0.468933</td>\n",
       "      <td>-0.177448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600536</th>\n",
       "      <td>-0.648704</td>\n",
       "      <td>-0.408761</td>\n",
       "      <td>-0.479886</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600588</th>\n",
       "      <td>0.327150</td>\n",
       "      <td>0.199457</td>\n",
       "      <td>0.255386</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600589</th>\n",
       "      <td>0.004017</td>\n",
       "      <td>0.018157</td>\n",
       "      <td>-0.028280</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600590</th>\n",
       "      <td>-0.192047</td>\n",
       "      <td>-0.194900</td>\n",
       "      <td>0.005705</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600595</th>\n",
       "      <td>-0.069449</td>\n",
       "      <td>-0.013864</td>\n",
       "      <td>-0.111170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600601</th>\n",
       "      <td>-0.071518</td>\n",
       "      <td>-0.047553</td>\n",
       "      <td>-0.047930</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600602</th>\n",
       "      <td>-0.020619</td>\n",
       "      <td>0.022803</td>\n",
       "      <td>-0.086844</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600718</th>\n",
       "      <td>-0.372869</td>\n",
       "      <td>-0.327002</td>\n",
       "      <td>-0.091732</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600728</th>\n",
       "      <td>-0.229074</td>\n",
       "      <td>-0.185714</td>\n",
       "      <td>-0.086721</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600756</th>\n",
       "      <td>-0.269601</td>\n",
       "      <td>-0.191433</td>\n",
       "      <td>-0.156336</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600770</th>\n",
       "      <td>-0.135974</td>\n",
       "      <td>-0.112889</td>\n",
       "      <td>-0.046169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600797</th>\n",
       "      <td>-0.134989</td>\n",
       "      <td>-0.070951</td>\n",
       "      <td>-0.128076</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600804</th>\n",
       "      <td>-0.353543</td>\n",
       "      <td>-0.371659</td>\n",
       "      <td>0.036233</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600845</th>\n",
       "      <td>0.292773</td>\n",
       "      <td>0.365709</td>\n",
       "      <td>-0.145873</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600850</th>\n",
       "      <td>-0.756647</td>\n",
       "      <td>-0.658652</td>\n",
       "      <td>-0.195991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601928</th>\n",
       "      <td>0.004650</td>\n",
       "      <td>-0.007193</td>\n",
       "      <td>0.023685</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603019</th>\n",
       "      <td>-0.584812</td>\n",
       "      <td>-0.860198</td>\n",
       "      <td>0.550773</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603138</th>\n",
       "      <td>-1.169107</td>\n",
       "      <td>-0.253536</td>\n",
       "      <td>-1.831142</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603881</th>\n",
       "      <td>-1.189179</td>\n",
       "      <td>-0.934114</td>\n",
       "      <td>-0.510130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1326 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       DIFF       DEA      MACD  CROSS_JC  CROSS_SC  ZERO\n",
       "date       code                                                          \n",
       "2018-01-02 000021  0.062832  0.034908  0.055848         0         0     0\n",
       "           000066 -0.103693 -0.164662  0.121938         0         0     0\n",
       "           000070 -0.298122 -0.297769 -0.000706         0         0     0\n",
       "           000611  0.085137  0.038461  0.093353         0         0     0\n",
       "           000836 -0.094147       NaN       NaN         0         0     0\n",
       "           000938  1.251292  0.414957  1.672669         0         0     0\n",
       "           000977  0.147007  0.089165  0.115685         0         0     0\n",
       "           002063 -0.000718 -0.041517  0.081599         0         0     0\n",
       "           002065 -0.330897 -0.311772 -0.038251         0         0     0\n",
       "           002093  0.071611  0.040341  0.062539         0         0     0\n",
       "           002095 -0.141841 -0.246130  0.208579         0         0     0\n",
       "           002195 -0.150915 -0.158826  0.015820         1         0     0\n",
       "           002197 -0.039920 -0.026400 -0.027039         0         0     0\n",
       "           002268 -0.282747 -0.095097 -0.375300         0         0     0\n",
       "           002315 -0.741722 -0.618039 -0.247365         0         0     0\n",
       "           002368 -0.415162 -0.345593 -0.139139         0         0     0\n",
       "           002396 -0.372709 -0.313333 -0.118752         0         0     0\n",
       "           002544 -0.707862 -0.720709  0.025694         1         0     0\n",
       "           002642 -0.391450 -0.475347  0.167794         0         0     0\n",
       "           002657 -0.449625 -0.507392  0.115533         0         0     0\n",
       "           300002 -0.121268 -0.130608  0.018681         0         0     0\n",
       "           300017  0.129319  0.011025  0.236589         0         0     0\n",
       "           300020 -0.333697 -0.260338 -0.146716         0         0     0\n",
       "           300036 -0.209178 -0.206702 -0.004952         0         0     0\n",
       "           300051 -0.106807 -0.081844 -0.049927         0         0     0\n",
       "           300188  0.250682  0.497559 -0.493753         0         0     0\n",
       "           300229 -0.144834  0.025245 -0.340159         0         0     0\n",
       "           300245 -0.144128 -0.121110 -0.046036         0         0     0\n",
       "           300290 -0.154023 -0.166551  0.025057         1         0     0\n",
       "           300297 -0.146436 -0.178916  0.064962         0         0     0\n",
       "...                     ...       ...       ...       ...       ...   ...\n",
       "2018-01-31 300366 -0.195730 -0.074191 -0.243077         0         0     0\n",
       "           300379 -0.184565 -0.186722  0.004316         0         0     0\n",
       "           300608 -0.347437 -0.370488  0.046103         0         0     0\n",
       "           600037 -0.050774 -0.090620  0.079691         0         0     0\n",
       "           600100 -0.030956 -0.033234  0.004556         0         0     0\n",
       "           600105 -0.058149 -0.063739  0.011182         0         0     0\n",
       "           600198 -0.463211 -0.483756  0.041092         0         0     0\n",
       "           600289 -1.171529 -1.205996  0.068934         0         0     0\n",
       "           600385 -0.338182 -0.307123 -0.062117         0         0     0\n",
       "           600410 -0.088613 -0.075791 -0.025643         0         1     0\n",
       "           600522 -0.557657 -0.468933 -0.177448         0         0     0\n",
       "           600536 -0.648704 -0.408761 -0.479886         0         0     0\n",
       "           600588  0.327150  0.199457  0.255386         0         0     0\n",
       "           600589  0.004017  0.018157 -0.028280         0         1     0\n",
       "           600590 -0.192047 -0.194900  0.005705         0         0     0\n",
       "           600595 -0.069449 -0.013864 -0.111170         0         0     0\n",
       "           600601 -0.071518 -0.047553 -0.047930         0         0     0\n",
       "           600602 -0.020619  0.022803 -0.086844         0         0     0\n",
       "           600718 -0.372869 -0.327002 -0.091732         0         0     0\n",
       "           600728 -0.229074 -0.185714 -0.086721         0         0     0\n",
       "           600756 -0.269601 -0.191433 -0.156336         0         0     0\n",
       "           600770 -0.135974 -0.112889 -0.046169         0         0     0\n",
       "           600797 -0.134989 -0.070951 -0.128076         0         0     0\n",
       "           600804 -0.353543 -0.371659  0.036233         0         0     0\n",
       "           600845  0.292773  0.365709 -0.145873         0         0     0\n",
       "           600850 -0.756647 -0.658652 -0.195991         0         0     0\n",
       "           601928  0.004650 -0.007193  0.023685         0         0     0\n",
       "           603019 -0.584812 -0.860198  0.550773         0         0     0\n",
       "           603138 -1.169107 -0.253536 -1.831142         0         0     0\n",
       "           603881 -1.189179 -0.934114 -0.510130         0         0     0\n",
       "\n",
       "[1326 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind.loc['2018-01',slice(None)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SETP4:选取回测的开始和结束日期,构建回测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'header': {'source': 'market', 'status': 200, 'code': '002396', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_lrNAwecE', 'trade_id': 'Trade_t57igN9j'}, 'body': {'order': {'price': 21.98, 'code': '002396', 'amount': 1000, 'date': '2018-01-04', 'datetime': '2018-01-04 15:00:00', 'towards': 1}, 'fee': {'commission': 5.495, 'tax': 0}}}\n",
      "10119.367798208848\n",
      "NOT ENOUGH MONEY FOR {'price': 21.98, 'code': '002396', 'amount': 1000, 'date': '2018-01-04', 'datetime': '2018-01-04 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300379', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_3JAvWrG9', 'trade_id': 'Trade_HrtuVjqg'}, 'body': {'order': {'price': 12.86, 'code': '300379', 'amount': 1000, 'date': '2018-01-04', 'datetime': '2018-01-04 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "10119.367798208848\n",
      "NOT ENOUGH MONEY FOR {'price': 12.86, 'code': '300379', 'amount': 1000, 'date': '2018-01-04', 'datetime': '2018-01-04 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300020', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_Tx4F9bvP', 'trade_id': 'Trade_rUBkZ8pc'}, 'body': {'order': {'price': 11.98, 'code': '300020', 'amount': 1000, 'date': '2018-01-08', 'datetime': '2018-01-08 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "314.3677982088484\n",
      "NOT ENOUGH MONEY FOR {'price': 11.98, 'code': '300020', 'amount': 1000, 'date': '2018-01-08', 'datetime': '2018-01-08 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600601', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_kZtPDvcn', 'trade_id': 'Trade_N8ZYpTBb'}, 'body': {'order': {'price': 3.68, 'code': '600601', 'amount': 1000, 'date': '2018-01-08', 'datetime': '2018-01-08 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "314.3677982088484\n",
      "NOT ENOUGH MONEY FOR {'price': 3.68, 'code': '600601', 'amount': 1000, 'date': '2018-01-08', 'datetime': '2018-01-08 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002315', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_Up8fxcgN', 'trade_id': 'Trade_L6SVhF9Q'}, 'body': {'order': {'price': 21.92, 'code': '002315', 'amount': 1000, 'date': '2018-01-09', 'datetime': '2018-01-09 15:00:00', 'towards': 1}, 'fee': {'commission': 5.48, 'tax': 0}}}\n",
      "314.3677982088484\n",
      "NOT ENOUGH MONEY FOR {'price': 21.92, 'code': '002315', 'amount': 1000, 'date': '2018-01-09', 'datetime': '2018-01-09 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002268', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_rOlcP2w7', 'trade_id': 'Trade_2RPv6Iab'}, 'body': {'order': {'price': 24.01, 'code': '002268', 'amount': 1000, 'date': '2018-01-10', 'datetime': '2018-01-10 15:00:00', 'towards': 1}, 'fee': {'commission': 6.002500000000001, 'tax': 0}}}\n",
      "314.3677982088484\n",
      "NOT ENOUGH MONEY FOR {'price': 24.01, 'code': '002268', 'amount': 1000, 'date': '2018-01-10', 'datetime': '2018-01-10 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300245', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_h2JqL3XE', 'trade_id': 'Trade_w1hXcYK3'}, 'body': {'order': {'price': 13.8, 'code': '300245', 'amount': 1000, 'date': '2018-01-10', 'datetime': '2018-01-10 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "314.3677982088484\n",
      "NOT ENOUGH MONEY FOR {'price': 13.8, 'code': '300245', 'amount': 1000, 'date': '2018-01-10', 'datetime': '2018-01-10 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300608', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_MdtenNDl', 'trade_id': 'Trade_B3TugEZj'}, 'body': {'order': {'price': 30.43, 'code': '300608', 'amount': 1000, 'date': '2018-01-10', 'datetime': '2018-01-10 15:00:00', 'towards': 1}, 'fee': {'commission': 7.6075, 'tax': 0}}}\n",
      "314.3677982088484\n",
      "NOT ENOUGH MONEY FOR {'price': 30.43, 'code': '300608', 'amount': 1000, 'date': '2018-01-10', 'datetime': '2018-01-10 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300229', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_8Zsi7Wt1', 'trade_id': 'Trade_DoeZ8T05'}, 'body': {'order': {'price': 15.19, 'code': '300229', 'amount': 1000, 'date': '2018-01-11', 'datetime': '2018-01-11 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6685.5152982088475\n",
      "NOT ENOUGH MONEY FOR {'price': 15.19, 'code': '300229', 'amount': 1000, 'date': '2018-01-11', 'datetime': '2018-01-11 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600588', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_DJsrGt2k', 'trade_id': 'Trade_UAw9dus4'}, 'body': {'order': {'price': 17.01, 'code': '600588', 'amount': 1000, 'date': '2018-01-11', 'datetime': '2018-01-11 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6685.5152982088475\n",
      "NOT ENOUGH MONEY FOR {'price': 17.01, 'code': '600588', 'amount': 1000, 'date': '2018-01-11', 'datetime': '2018-01-11 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600850', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_NHC0zRoM', 'trade_id': 'Trade_7CvEIQqD'}, 'body': {'order': {'price': 19.55, 'code': '600850', 'amount': 1000, 'date': '2018-01-11', 'datetime': '2018-01-11 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "2990.5152982088475\n",
      "NOT ENOUGH MONEY FOR {'price': 19.55, 'code': '600850', 'amount': 1000, 'date': '2018-01-11', 'datetime': '2018-01-11 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '603138', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_r3Y6s2q5', 'trade_id': 'Trade_4PCehr2A'}, 'body': {'order': {'price': 51.75, 'code': '603138', 'amount': 1000, 'date': '2018-01-11', 'datetime': '2018-01-11 15:00:00', 'towards': 1}, 'fee': {'commission': 12.937500000000002, 'tax': 0}}}\n",
      "2990.5152982088475\n",
      "NOT ENOUGH MONEY FOR {'price': 51.75, 'code': '603138', 'amount': 1000, 'date': '2018-01-11', 'datetime': '2018-01-11 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002095', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_cfxkYvH1', 'trade_id': 'Trade_MsNdhpt2'}, 'body': {'order': {'price': 34.75, 'code': '002095', 'amount': 1000, 'date': '2018-01-12', 'datetime': '2018-01-12 15:00:00', 'towards': 1}, 'fee': {'commission': 8.6875, 'tax': 0}}}\n",
      "2990.5152982088475\n",
      "NOT ENOUGH MONEY FOR {'price': 34.75, 'code': '002095', 'amount': 1000, 'date': '2018-01-12', 'datetime': '2018-01-12 15:00:00', 'towards': 1}\n",
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      "20940.84255972586\n",
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      "5475.842559725861\n",
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      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600797', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_ztE0ZhCk', 'trade_id': 'Trade_Q5qzslCZ'}, 'body': {'order': {'price': 12.47, 'code': '600797', 'amount': 1000, 'date': '2018-01-24', 'datetime': '2018-01-24 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600845', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_Ar3cQfkq', 'trade_id': 'Trade_F9rbzjoH'}, 'body': {'order': {'price': 20.26, 'code': '600845', 'amount': 1000, 'date': '2018-01-24', 'datetime': '2018-01-24 15:00:00', 'towards': 1}, 'fee': {'commission': 5.064876355325, 'tax': 0}}}\n",
      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '603019', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_wNdCl9ZQ', 'trade_id': 'Trade_wItoXnMA'}, 'body': {'order': {'price': 41.0, 'code': '603019', 'amount': 1000, 'date': '2018-01-24', 'datetime': '2018-01-24 15:00:00', 'towards': 1}, 'fee': {'commission': 10.25043064025, 'tax': 0}}}\n",
      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600198', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_FSMeIcfK', 'trade_id': 'Trade_W3AYHP0o'}, 'body': {'order': {'price': 10.36, 'code': '600198', 'amount': 1000, 'date': '2018-01-25', 'datetime': '2018-01-25 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600590', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_QjVJ9tOB', 'trade_id': 'Trade_MguwqIBF'}, 'body': {'order': {'price': 10.37, 'code': '600590', 'amount': 1000, 'date': '2018-01-25', 'datetime': '2018-01-25 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '002368', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_IOJyhtWX', 'trade_id': 'Trade_geunGNpf'}, 'body': {'order': {'price': 23.35, 'code': '002368', 'amount': 1000, 'date': '2018-01-26', 'datetime': '2018-01-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5.8375, 'tax': 0}}}\n",
      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '300051', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_7FmGlk3I', 'trade_id': 'Trade_OqzaitQW'}, 'body': {'order': {'price': 11.04, 'code': '300051', 'amount': 1000, 'date': '2018-01-26', 'datetime': '2018-01-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "5475.842559725861\n",
      "NOT ENOUGH MONEY FOR {'price': 11.04, 'code': '300051', 'amount': 1000, 'date': '2018-01-26', 'datetime': '2018-01-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300188', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_NPMQybmo', 'trade_id': 'Trade_lUySOk3V'}, 'body': {'order': {'price': 20.49, 'code': '300188', 'amount': 1000, 'date': '2018-01-26', 'datetime': '2018-01-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5.1225, 'tax': 0}}}\n",
      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '300229', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_aQnqAErp', 'trade_id': 'Trade_FXcOEhPj'}, 'body': {'order': {'price': 14.04, 'code': '300229', 'amount': 1000, 'date': '2018-01-26', 'datetime': '2018-01-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "5475.842559725861\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600601', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_GCfetr7L', 'trade_id': 'Trade_AXE5cplj'}, 'body': {'order': {'price': 3.6, 'code': '600601', 'amount': 1000, 'date': '2018-01-26', 'datetime': '2018-01-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "430.84255972586107\n",
      "NOT ENOUGH MONEY FOR {'price': 3.6, 'code': '600601', 'amount': 1000, 'date': '2018-01-26', 'datetime': '2018-01-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300608', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_dUA3gJQC', 'trade_id': 'Trade_ynXYGkjZ'}, 'body': {'order': {'price': 30.89, 'code': '300608', 'amount': 1000, 'date': '2018-01-30', 'datetime': '2018-01-30 15:00:00', 'towards': 1}, 'fee': {'commission': 7.7225, 'tax': 0}}}\n",
      "15088.785059725862\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '603019', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_AIs392dp', 'trade_id': 'Trade_bFe4aiqW'}, 'body': {'order': {'price': 37.63, 'code': '603019', 'amount': 1000, 'date': '2018-02-14', 'datetime': '2018-02-14 15:00:00', 'towards': 1}, 'fee': {'commission': 9.40704077745, 'tax': 0}}}\n",
      "29779.555174546564\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '000938', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_g9bzUtoI', 'trade_id': 'Trade_tVHibmE4'}, 'body': {'order': {'price': 59.38, 'code': '000938', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}, 'fee': {'commission': 14.845, 'tax': 0}}}\n",
      "29779.555174546564\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '002642', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_oRLIfzdD', 'trade_id': 'Trade_cdDf3hjk'}, 'body': {'order': {'price': 10.67, 'code': '002642', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
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      "6343.697674546565\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '300229', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_aTmGsDt7', 'trade_id': 'Trade_HlQoVADt'}, 'body': {'order': {'price': 12.36, 'code': '300229', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600385', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_6UFrvLVY', 'trade_id': 'Trade_fYnEaeDI'}, 'body': {'order': {'price': 10.24, 'code': '600385', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 10.24, 'code': '600385', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600410', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_RXWazvh6', 'trade_id': 'Trade_IsQBAuLT'}, 'body': {'order': {'price': 9.47, 'code': '600410', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 9.47, 'code': '600410', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600718', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_HOiyRtrD', 'trade_id': 'Trade_fZkMXphs'}, 'body': {'order': {'price': 12.26, 'code': '600718', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 12.26, 'code': '600718', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600728', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_mWx3sKpI', 'trade_id': 'Trade_Tz2NkdyL'}, 'body': {'order': {'price': 6.99, 'code': '600728', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 6.99, 'code': '600728', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600850', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_D7BeKEo0', 'trade_id': 'Trade_utIcm9Kq'}, 'body': {'order': {'price': 15.07, 'code': '600850', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 15.07, 'code': '600850', 'amount': 1000, 'date': '2018-02-22', 'datetime': '2018-02-22 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002093', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_NeCXRYUy', 'trade_id': 'Trade_EwdKOneX'}, 'body': {'order': {'price': 8.63, 'code': '002093', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 8.63, 'code': '002093', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002197', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_EtBw0qpy', 'trade_id': 'Trade_AsTbZK0e'}, 'body': {'order': {'price': 10.28, 'code': '002197', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 10.28, 'code': '002197', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300002', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_Ce52BaMZ', 'trade_id': 'Trade_862UWGay'}, 'body': {'order': {'price': 6.34, 'code': '300002', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 6.34, 'code': '300002', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300297', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_73PuMFbr', 'trade_id': 'Trade_klMjTJvq'}, 'body': {'order': {'price': 8.96, 'code': '300297', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 8.96, 'code': '300297', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600198', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_BWP4ohtG', 'trade_id': 'Trade_15LcPKQB'}, 'body': {'order': {'price': 7.36, 'code': '600198', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600536', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_5kJE0CUa', 'trade_id': 'Trade_8ENRSTKz'}, 'body': {'order': {'price': 12.23, 'code': '600536', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
      "NOT ENOUGH MONEY FOR {'price': 12.23, 'code': '600536', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600590', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_q5S7AlNk', 'trade_id': 'Trade_pf5wXq03'}, 'body': {'order': {'price': 8.95, 'code': '600590', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "6343.697674546565\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600601', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_BV9yIYai', 'trade_id': 'Trade_eINsYyx4'}, 'body': {'order': {'price': 2.89, 'code': '600601', 'amount': 1000, 'date': '2018-02-23', 'datetime': '2018-02-23 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
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     "text": [
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      "1958.6976745465654\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '000021', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_ofVl6Opg', 'trade_id': 'Trade_GlaXxOnC'}, 'body': {'order': {'price': 8.5, 'code': '000021', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 8.5, 'code': '000021', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '000070', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_QNID08SW', 'trade_id': 'Trade_9cxvdnhy'}, 'body': {'order': {'price': 8.9, 'code': '000070', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 8.9, 'code': '000070', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002065', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_GcqmLNEY', 'trade_id': 'Trade_3E1PjvsA'}, 'body': {'order': {'price': 8.03, 'code': '002065', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 8.03, 'code': '002065', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002315', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_OflaokTc', 'trade_id': 'Trade_y4uQ2GBF'}, 'body': {'order': {'price': 18.96, 'code': '002315', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 18.96, 'code': '002315', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300020', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_AGHqZCji', 'trade_id': 'Trade_2ysOJmWn'}, 'body': {'order': {'price': 10.14, 'code': '300020', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 10.14, 'code': '300020', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300245', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_aA40ljwF', 'trade_id': 'Trade_3TFAh2mz'}, 'body': {'order': {'price': 12.54, 'code': '300245', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 12.54, 'code': '300245', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300366', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_UdlfwhO2', 'trade_id': 'Trade_6RT8JhIB'}, 'body': {'order': {'price': 9.82, 'code': '300366', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 9.82, 'code': '300366', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300379', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_t4RumaOl', 'trade_id': 'Trade_xlizjApE'}, 'body': {'order': {'price': 11.11, 'code': '300379', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 11.11, 'code': '300379', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300608', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_F1zu4tNI', 'trade_id': 'Trade_Fy4GZUrx'}, 'body': {'order': {'price': 26.37, 'code': '300608', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 6.5925, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 26.37, 'code': '300608', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600105', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_tHFwAyvg', 'trade_id': 'Trade_rn7lb9vg'}, 'body': {'order': {'price': 5.42, 'code': '600105', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 5.42, 'code': '600105', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600602', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_Xwo6juyT', 'trade_id': 'Trade_NHbkcV0o'}, 'body': {'order': {'price': 6.73, 'code': '600602', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 6.73, 'code': '600602', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600756', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_IB3PeEor', 'trade_id': 'Trade_oSMCmA45'}, 'body': {'order': {'price': 14.56, 'code': '600756', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 14.56, 'code': '600756', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600770', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_7ArElvDQ', 'trade_id': 'Trade_M3qs9zkQ'}, 'body': {'order': {'price': 6.44, 'code': '600770', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 6.44, 'code': '600770', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600797', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_N5MgaG2i', 'trade_id': 'Trade_XLs5lagE'}, 'body': {'order': {'price': 10.75, 'code': '600797', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 10.75, 'code': '600797', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n"
     ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 7.71, 'code': '601928', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '603881', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_VmZa10Sk', 'trade_id': 'Trade_E4kRyeXT'}, 'body': {'order': {'price': 35.38, 'code': '603881', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}, 'fee': {'commission': 8.844286727175001, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 35.38, 'code': '603881', 'amount': 1000, 'date': '2018-02-26', 'datetime': '2018-02-26 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '000066', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_eFBVAwIl', 'trade_id': 'Trade_PyXxz4mQ'}, 'body': {'order': {'price': 6.6, 'code': '000066', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 6.6, 'code': '000066', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '000971', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_KBLsRQM3', 'trade_id': 'Trade_f5V6kvuR'}, 'body': {'order': {'price': 5.82, 'code': '000971', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 5.82, 'code': '000971', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002063', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_abvk6ZjN', 'trade_id': 'Trade_NjPBaqcn'}, 'body': {'order': {'price': 10.53, 'code': '002063', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 10.53, 'code': '002063', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002095', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_0f6hntbx', 'trade_id': 'Trade_JdhO3ori'}, 'body': {'order': {'price': 42.68, 'code': '002095', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 10.67, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 42.68, 'code': '002095', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002195', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_xV7BlCq8', 'trade_id': 'Trade_PJY9xOHR'}, 'body': {'order': {'price': 5.86, 'code': '002195', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 5.86, 'code': '002195', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002544', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_3qfBSiMX', 'trade_id': 'Trade_xCPm7EgR'}, 'body': {'order': {'price': 13.31, 'code': '002544', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 13.31, 'code': '002544', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002657', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_68oQa97A', 'trade_id': 'Trade_BJnoHIrZ'}, 'body': {'order': {'price': 19.11, 'code': '002657', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 19.11, 'code': '002657', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300017', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_vFEKM5ZC', 'trade_id': 'Trade_GHFZUYda'}, 'body': {'order': {'price': 12.3, 'code': '300017', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 12.3, 'code': '300017', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300051', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_Dkd3Wgop', 'trade_id': 'Trade_ZsgwTVED'}, 'body': {'order': {'price': 9.71, 'code': '300051', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 9.71, 'code': '300051', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600037', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_VCR2kKTi', 'trade_id': 'Trade_kLxPOW8C'}, 'body': {'order': {'price': 12.23, 'code': '600037', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 12.23, 'code': '600037', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600589', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_M3YejIbP', 'trade_id': 'Trade_ZxA89sQp'}, 'body': {'order': {'price': 5.58, 'code': '600589', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 5.58, 'code': '600589', 'amount': 1000, 'date': '2018-02-27', 'datetime': '2018-02-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '000836', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_FiPxOare', 'trade_id': 'Trade_YOonFcEu'}, 'body': {'order': {'price': 5.32, 'code': '000836', 'amount': 1000, 'date': '2018-02-28', 'datetime': '2018-02-28 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1958.6976745465654\n",
      "NOT ENOUGH MONEY FOR {'price': 5.32, 'code': '000836', 'amount': 1000, 'date': '2018-02-28', 'datetime': '2018-02-28 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '603019', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_ESQJ32aX', 'trade_id': 'Trade_3e2OtLTK'}, 'body': {'order': {'price': 53.81, 'code': '603019', 'amount': 1000, 'date': '2018-03-27', 'datetime': '2018-03-27 15:00:00', 'towards': 1}, 'fee': {'commission': 13.4518187881, 'tax': 0}}}\n",
      "32228.835924940286\n",
      "NOT ENOUGH MONEY FOR {'price': 53.81, 'code': '603019', 'amount': 1000, 'date': '2018-03-27', 'datetime': '2018-03-27 15:00:00', 'towards': 1}\n"
     ]
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     "text": [
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      "32228.835924940286\n",
      "NOT ENOUGH MONEY FOR {'price': 42.38, 'code': '603138', 'amount': 1000, 'date': '2018-03-27', 'datetime': '2018-03-27 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002315', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_TEXbjx4R', 'trade_id': 'Trade_pm8sHLXM'}, 'body': {'order': {'price': 20.86, 'code': '002315', 'amount': 1000, 'date': '2018-03-29', 'datetime': '2018-03-29 15:00:00', 'towards': 1}, 'fee': {'commission': 5.215, 'tax': 0}}}\n",
      "13326.280924940282\n",
      "NOT ENOUGH MONEY FOR {'price': 20.86, 'code': '002315', 'amount': 1000, 'date': '2018-03-29', 'datetime': '2018-03-29 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600718', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_gyGnkmBh', 'trade_id': 'Trade_X2Gc9usw'}, 'body': {'order': {'price': 14.19, 'code': '600718', 'amount': 1000, 'date': '2018-03-29', 'datetime': '2018-03-29 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600850', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_B6Ra3uqO', 'trade_id': 'Trade_UhTLnAG9'}, 'body': {'order': {'price': 18.08, 'code': '600850', 'amount': 1000, 'date': '2018-03-29', 'datetime': '2018-03-29 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '000070', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_P9RNlLES', 'trade_id': 'Trade_s4whB9IU'}, 'body': {'order': {'price': 9.5, 'code': '000070', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '000938', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_h6SVHF3w', 'trade_id': 'Trade_mRPGE4tl'}, 'body': {'order': {'price': 72.85, 'code': '000938', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 18.2125, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '002063', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_SqGTD9wM', 'trade_id': 'Trade_Z0txVuWH'}, 'body': {'order': {'price': 11.37, 'code': '002063', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
      "NOT ENOUGH MONEY FOR {'price': 11.37, 'code': '002063', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002093', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_ehGYdHnL', 'trade_id': 'Trade_BaTewSJx'}, 'body': {'order': {'price': 9.17, 'code': '002093', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
      "NOT ENOUGH MONEY FOR {'price': 9.17, 'code': '002093', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002396', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_DBwUOnAQ', 'trade_id': 'Trade_3hU2Qdsy'}, 'body': {'order': {'price': 24.43, 'code': '002396', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 6.1075, 'tax': 0}}}\n",
      "3331.2809249402817\n",
      "NOT ENOUGH MONEY FOR {'price': 24.43, 'code': '002396', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002657', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_1FrTojVl', 'trade_id': 'Trade_4QOzZnwu'}, 'body': {'order': {'price': 20.61, 'code': '002657', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5.1525, 'tax': 0}}}\n",
      "3331.2809249402817\n",
      "NOT ENOUGH MONEY FOR {'price': 20.61, 'code': '002657', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300017', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_9uPkZE7O', 'trade_id': 'Trade_zBni0MQX'}, 'body': {'order': {'price': 14.95, 'code': '300017', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
      "NOT ENOUGH MONEY FOR {'price': 14.95, 'code': '300017', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '300020', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_l3HwQbNq', 'trade_id': 'Trade_fQAbdX7q'}, 'body': {'order': {'price': 11.95, 'code': '300020', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '300366', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_T3PmqSdz', 'trade_id': 'Trade_ChJf7DRO'}, 'body': {'order': {'price': 12.19, 'code': '300366', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
      "NOT ENOUGH MONEY FOR {'price': 12.19, 'code': '300366', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600105', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_E0tudRfB', 'trade_id': 'Trade_45lrtqQI'}, 'body': {'order': {'price': 5.62, 'code': '600105', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
      "NOT ENOUGH MONEY FOR {'price': 5.62, 'code': '600105', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '600198', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_4ImyK2bF', 'trade_id': 'Trade_zovXEkKu'}, 'body': {'order': {'price': 8.22, 'code': '600198', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
      "NOT ENOUGH MONEY FOR {'price': 8.22, 'code': '600198', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}\n"
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      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600797', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_dsJAKL26', 'trade_id': 'Trade_dvNijCZw'}, 'body': {'order': {'price': 12.48, 'code': '600797', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600845', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_muVL8sAS', 'trade_id': 'Trade_8Pp6ZqbA'}, 'body': {'order': {'price': 27.54, 'code': '600845', 'amount': 1000, 'date': '2018-03-30', 'datetime': '2018-03-30 15:00:00', 'towards': 1}, 'fee': {'commission': 6.884949743675, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '000021', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_Wg0zHc2j', 'trade_id': 'Trade_3P8cLBo7'}, 'body': {'order': {'price': 8.94, 'code': '000021', 'amount': 1000, 'date': '2018-04-02', 'datetime': '2018-04-02 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '002065', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_8YxZhV5j', 'trade_id': 'Trade_Y3cEPUh0'}, 'body': {'order': {'price': 8.57, 'code': '002065', 'amount': 1000, 'date': '2018-04-02', 'datetime': '2018-04-02 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "3331.2809249402817\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600410', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_FwuMdfri', 'trade_id': 'Trade_p5rQ3whN'}, 'body': {'order': {'price': 12.22, 'code': '600410', 'amount': 1000, 'date': '2018-04-02', 'datetime': '2018-04-02 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "7600.294674940284\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '002195', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_iTlHBaZm', 'trade_id': 'Trade_hfv9sz5g'}, 'body': {'order': {'price': 5.85, 'code': '002195', 'amount': 1000, 'date': '2018-04-03', 'datetime': '2018-04-03 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "4375.294674940284\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '002197', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_9aUGrY7M', 'trade_id': 'Trade_aSjGFqpr'}, 'body': {'order': {'price': 10.36, 'code': '002197', 'amount': 1000, 'date': '2018-04-03', 'datetime': '2018-04-03 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "4375.294674940284\n",
      "NOT ENOUGH MONEY FOR {'price': 10.36, 'code': '002197', 'amount': 1000, 'date': '2018-04-03', 'datetime': '2018-04-03 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002268', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_CvmrSJN6', 'trade_id': 'Trade_g6qQec2i'}, 'body': {'order': {'price': 30.3, 'code': '002268', 'amount': 1000, 'date': '2018-04-03', 'datetime': '2018-04-03 15:00:00', 'towards': 1}, 'fee': {'commission': 7.575, 'tax': 0}}}\n",
      "4375.294674940284\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600770', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_SEwXGpkM', 'trade_id': 'Trade_wT5xSeU1'}, 'body': {'order': {'price': 7.15, 'code': '600770', 'amount': 1000, 'date': '2018-04-03', 'datetime': '2018-04-03 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "4375.294674940284\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '601928', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_szvnMAGV', 'trade_id': 'Trade_JRVqc58u'}, 'body': {'order': {'price': 7.39, 'code': '601928', 'amount': 1000, 'date': '2018-04-11', 'datetime': '2018-04-11 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '300020', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_2hIwomPb', 'trade_id': 'Trade_BNITWYsb'}, 'body': {'order': {'price': 11.89, 'code': '300020', 'amount': 1000, 'date': '2018-04-13', 'datetime': '2018-04-13 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "7154.838530748246\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600590', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_FJrnhKLY', 'trade_id': 'Trade_cIOHmgR1'}, 'body': {'order': {'price': 12.11, 'code': '600590', 'amount': 1000, 'date': '2018-04-13', 'datetime': '2018-04-13 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1679.8385307482458\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '002065', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_QPafLgrN', 'trade_id': 'Trade_Wxu2aVwJ'}, 'body': {'order': {'price': 8.4, 'code': '002065', 'amount': 1000, 'date': '2018-04-16', 'datetime': '2018-04-16 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "713.0335307482455\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '300188', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_JX3g9LW2', 'trade_id': 'Trade_6YcnFjDS'}, 'body': {'order': {'price': 32.27, 'code': '300188', 'amount': 1000, 'date': '2018-04-16', 'datetime': '2018-04-16 15:00:00', 'towards': 1}, 'fee': {'commission': 8.067500000000003, 'tax': 0}}}\n",
      "713.0335307482455\n",
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      "713.0335307482455\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600756', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_X4BUtDZi', 'trade_id': 'Trade_oE3vKxCS'}, 'body': {'order': {'price': 18.96, 'code': '600756', 'amount': 1000, 'date': '2018-04-16', 'datetime': '2018-04-16 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "713.0335307482455\n",
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      "713.0335307482455\n",
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      "713.0335307482455\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '000938', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_UC10OQHh', 'trade_id': 'Trade_ob9pnNJf'}, 'body': {'order': {'price': 79.42, 'code': '000938', 'amount': 1000, 'date': '2018-04-18', 'datetime': '2018-04-18 15:00:00', 'towards': 1}, 'fee': {'commission': 19.855, 'tax': 0}}}\n",
      "51466.751313649074\n",
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      "1596.7513136490743\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '002544', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_wFHYvsAi', 'trade_id': 'Trade_OBdRPXpy'}, 'body': {'order': {'price': 17.42, 'code': '002544', 'amount': 1000, 'date': '2018-04-19', 'datetime': '2018-04-19 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1596.7513136490743\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '300017', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_XErDw50K', 'trade_id': 'Trade_1yMvScPp'}, 'body': {'order': {'price': 14.85, 'code': '300017', 'amount': 1000, 'date': '2018-04-19', 'datetime': '2018-04-19 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1596.7513136490743\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '300366', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_nwaF80A3', 'trade_id': 'Trade_rWi6ucQd'}, 'body': {'order': {'price': 12.2, 'code': '300366', 'amount': 1000, 'date': '2018-04-19', 'datetime': '2018-04-19 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1596.7513136490743\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600410', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_gfaZ7v45', 'trade_id': 'Trade_kLjlXH4z'}, 'body': {'order': {'price': 12.58, 'code': '600410', 'amount': 1000, 'date': '2018-04-19', 'datetime': '2018-04-19 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1596.7513136490743\n",
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      "{'header': {'source': 'market', 'status': 200, 'code': '600797', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_X5xnRd6s', 'trade_id': 'Trade_XyuMroSN'}, 'body': {'order': {'price': 12.56, 'code': '600797', 'amount': 1000, 'date': '2018-04-19', 'datetime': '2018-04-19 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "1596.7513136490743\n",
      "NOT ENOUGH MONEY FOR {'price': 12.56, 'code': '600797', 'amount': 1000, 'date': '2018-04-19', 'datetime': '2018-04-19 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002197', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_IjFohMZ6', 'trade_id': 'Trade_H3FT0iuq'}, 'body': {'order': {'price': 10.35, 'code': '002197', 'amount': 1000, 'date': '2018-04-25', 'datetime': '2018-04-25 15:00:00', 'towards': 1}, 'fee': {'commission': 5, 'tax': 0}}}\n",
      "4221.566313649076\n",
      "NOT ENOUGH MONEY FOR {'price': 10.35, 'code': '002197', 'amount': 1000, 'date': '2018-04-25', 'datetime': '2018-04-25 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002315', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_Fe4lIRuQ', 'trade_id': 'Trade_9Qp1vO8e'}, 'body': {'order': {'price': 20.9, 'code': '002315', 'amount': 1000, 'date': '2018-04-25', 'datetime': '2018-04-25 15:00:00', 'towards': 1}, 'fee': {'commission': 5.225, 'tax': 0}}}\n",
      "4221.566313649076\n",
      "NOT ENOUGH MONEY FOR {'price': 20.9, 'code': '002315', 'amount': 1000, 'date': '2018-04-25', 'datetime': '2018-04-25 15:00:00', 'towards': 1}\n",
      "{'header': {'source': 'market', 'status': 200, 'code': '002657', 'session': {'user': None, 'strategy': None, 'account': 'JCSC_EXAMPLE'}, 'order_id': 'Order_oMpmt5yC', 'trade_id': 'Trade_BHdNf3EW'}, 'body': {'order': {'price': 20.79, 'code': '002657', 'amount': 1000, 'date': '2018-04-26', 'datetime': '2018-04-26 15:00:00', 'towards': 1}, 'fee': {'commission': 5.1975, 'tax': 0}}}\n",
      "1350.1439097803013\n",
      "NOT ENOUGH MONEY FOR {'price': 20.79, 'code': '002657', 'amount': 1000, 'date': '2018-04-26', 'datetime': '2018-04-26 15:00:00', 'towards': 1}\n"
     ]
    }
   ],
   "source": [
    "data_forbacktest=data.select_time('2018-01-01','2018-05-01')\n",
    "\n",
    "\n",
    "for items in data_forbacktest.panel_gen:\n",
    "    for item in items.security_gen:\n",
    "        daily_ind=ind.loc[item.index]\n",
    "        if daily_ind.CROSS_JC.iloc[0]>0:\n",
    "            order=Account.send_order(\n",
    "                code=item.data.code[0], \n",
    "                time=item.data.date[0], \n",
    "                amount=1000, \n",
    "                towards=QA.ORDER_DIRECTION.BUY, \n",
    "                price=0, \n",
    "                order_model=QA.ORDER_MODEL.CLOSE, \n",
    "                amount_model=QA.AMOUNT_MODEL.BY_AMOUNT\n",
    "                )\n",
    "            Account.receive_deal(Broker.receive_order(QA.QA_Event(order=order,market_data=item)))\n",
    "        elif daily_ind.CROSS_SC.iloc[0]>0:\n",
    "            if Account.sell_available.get(item.code[0], 0)>0:\n",
    "                order=Account.send_order(\n",
    "                    code=item.data.code[0], \n",
    "                    time=item.data.date[0], \n",
    "                    amount=Account.sell_available.get(item.code[0], 0), \n",
    "                    towards=QA.ORDER_DIRECTION.SELL, \n",
    "                    price=0, \n",
    "                    order_model=QA.ORDER_MODEL.MARKET, \n",
    "                    amount_model=QA.AMOUNT_MODEL.BY_AMOUNT\n",
    "                    )\n",
    "                Account.receive_deal(Broker.receive_order(QA.QA_Event(order=order,market_data=item)))\n",
    "    Account.settle()\n",
    "            \n",
    "        #break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP5: 分析账户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['2018-01-02 15:00:00',\n",
       "  '002195',\n",
       "  5.91,\n",
       "  1000.0,\n",
       "  'Order_dRhVu53P',\n",
       "  'Trade_Xn1l6LPZ',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-02 15:00:00',\n",
       "  '002544',\n",
       "  15.85,\n",
       "  1000.0,\n",
       "  'Order_ykw3N6uH',\n",
       "  'Trade_cOpw7vmN',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-02 15:00:00',\n",
       "  '300290',\n",
       "  8.71,\n",
       "  1000.0,\n",
       "  'Order_FfO0nlsc',\n",
       "  'Trade_sZ61KjYP',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-02 15:00:00',\n",
       "  '600105',\n",
       "  6.58,\n",
       "  1000.0,\n",
       "  'Order_UvYiHIK1',\n",
       "  'Trade_lq6b7k9Z',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-02 15:00:00',\n",
       "  '600797',\n",
       "  11.87,\n",
       "  1000.0,\n",
       "  'Order_yGimNbhn',\n",
       "  'Trade_fPUCQ01t',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-03 15:00:00',\n",
       "  '000070',\n",
       "  9.52,\n",
       "  1000.0,\n",
       "  'Order_l97R514S',\n",
       "  'Trade_OhtnrxDC',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-03 15:00:00',\n",
       "  '002065',\n",
       "  8.53,\n",
       "  1000.0,\n",
       "  'Order_5MvgxTLt',\n",
       "  'Trade_rm7aYq5w',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-03 15:00:00',\n",
       "  '300036',\n",
       "  15.78,\n",
       "  1000.0,\n",
       "  'Order_cKI5HDl8',\n",
       "  'Trade_omAkLOGx',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-03 15:00:00',\n",
       "  '600198',\n",
       "  11.65,\n",
       "  1000.0,\n",
       "  'Order_bTHhagWP',\n",
       "  'Trade_AHe9zjXL',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-03 15:00:00',\n",
       "  '600718',\n",
       "  15.0,\n",
       "  1000.0,\n",
       "  'Order_hHLinCrl',\n",
       "  'Trade_gGVZT4uc',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-03 15:00:00',\n",
       "  '600804',\n",
       "  17.93,\n",
       "  1000.0,\n",
       "  'Order_4uv6fj8Q',\n",
       "  'Trade_FvJX1o0r',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-03 15:00:00',\n",
       "  '600850',\n",
       "  19.95,\n",
       "  1000.0,\n",
       "  'Order_hCyLzeHQ',\n",
       "  'Trade_Dlg8ahqj',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-03 15:00:00',\n",
       "  '603019',\n",
       "  42.53,\n",
       "  1000.0,\n",
       "  'Order_U3RVGrve',\n",
       "  'Trade_okmF7CPM',\n",
       "  'JCSC_EXAMPLE',\n",
       "  10.63220179115,\n",
       "  0.0],\n",
       " ['2018-01-05 15:00:00',\n",
       "  '300051',\n",
       "  9.8,\n",
       "  1000.0,\n",
       "  'Order_9qoJpiQZ',\n",
       "  'Trade_MZ4NOVE5',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-10 09:30:00',\n",
       "  '600850',\n",
       "  19.23,\n",
       "  -1000.0,\n",
       "  'Order_XRiYpusq',\n",
       "  'Trade_xY9P4eGr',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  28.8525],\n",
       " ['2018-01-11 15:00:00',\n",
       "  '002197',\n",
       "  12.82,\n",
       "  1000.0,\n",
       "  'Order_pkabzXw9',\n",
       "  'Trade_Qk7bjUyo',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-11 15:00:00',\n",
       "  '600601',\n",
       "  3.69,\n",
       "  1000.0,\n",
       "  'Order_tqDujrOf',\n",
       "  'Trade_CizLsXG8',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-15 09:30:00',\n",
       "  '002197',\n",
       "  12.06,\n",
       "  -1000.0,\n",
       "  'Order_wKERgClG',\n",
       "  'Trade_E3UtcdWy',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  18.090000000000003],\n",
       " ['2018-01-15 09:30:00',\n",
       "  '300036',\n",
       "  14.49,\n",
       "  -1000.0,\n",
       "  'Order_frBKjRAa',\n",
       "  'Trade_lahUpuOV',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  21.732952850325],\n",
       " ['2018-01-15 09:30:00',\n",
       "  '300290',\n",
       "  8.08,\n",
       "  -1000.0,\n",
       "  'Order_xS0pjWz9',\n",
       "  'Trade_TSf9Mva1',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  12.120000000000001],\n",
       " ['2018-01-15 09:30:00',\n",
       "  '600198',\n",
       "  10.61,\n",
       "  -1000.0,\n",
       "  'Order_YvLncXNH',\n",
       "  'Trade_6fX9pcoH',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  15.915],\n",
       " ['2018-01-15 09:30:00',\n",
       "  '600601',\n",
       "  3.59,\n",
       "  -1000.0,\n",
       "  'Order_1q2mNkKh',\n",
       "  'Trade_PtGpJWZF',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  5.3925],\n",
       " ['2018-01-15 09:30:00',\n",
       "  '603019',\n",
       "  37.48,\n",
       "  -1000.0,\n",
       "  'Order_An5xod2g',\n",
       "  'Trade_D1YwqSTA',\n",
       "  'JCSC_EXAMPLE',\n",
       "  9.3696122332375,\n",
       "  56.217673399425],\n",
       " ['2018-01-16 09:30:00',\n",
       "  '002544',\n",
       "  14.62,\n",
       "  -1000.0,\n",
       "  'Order_p5FaMcRr',\n",
       "  'Trade_krqRKVn9',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  21.930000000000003],\n",
       " ['2018-01-16 09:30:00',\n",
       "  '600718',\n",
       "  14.27,\n",
       "  -1000.0,\n",
       "  'Order_P6RCfBtL',\n",
       "  'Trade_xvaCt3W1',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  21.3975],\n",
       " ['2018-01-16 09:30:00',\n",
       "  '600804',\n",
       "  16.51,\n",
       "  -1000.0,\n",
       "  'Order_wo9xpg6j',\n",
       "  'Trade_dCM4bzyl',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  24.765],\n",
       " ['2018-01-17 15:00:00',\n",
       "  '300297',\n",
       "  9.56,\n",
       "  1000.0,\n",
       "  'Order_oEip0DhY',\n",
       "  'Trade_grtUPdf5',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-19 09:30:00',\n",
       "  '300051',\n",
       "  9.81,\n",
       "  -1000.0,\n",
       "  'Order_ljt8XCM9',\n",
       "  'Trade_KnYN75Za',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  14.715],\n",
       " ['2018-01-19 15:00:00',\n",
       "  '600804',\n",
       "  17.22,\n",
       "  1000.0,\n",
       "  'Order_NiV4FmYK',\n",
       "  'Trade_L3CkFK0d',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-22 15:00:00',\n",
       "  '002095',\n",
       "  38.61,\n",
       "  1000.0,\n",
       "  'Order_PeK5RBIJ',\n",
       "  'Trade_VmH1B8M2',\n",
       "  'JCSC_EXAMPLE',\n",
       "  9.6525,\n",
       "  0.0],\n",
       " ['2018-01-22 15:00:00',\n",
       "  '300245',\n",
       "  13.76,\n",
       "  1000.0,\n",
       "  'Order_R1H9awSx',\n",
       "  'Trade_wZcyAOMe',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-23 15:00:00',\n",
       "  '002544',\n",
       "  14.93,\n",
       "  1000.0,\n",
       "  'Order_NFR3J7Ye',\n",
       "  'Trade_AD6XE3fJ',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-23 15:00:00',\n",
       "  '300366',\n",
       "  11.89,\n",
       "  1000.0,\n",
       "  'Order_qvy07wut',\n",
       "  'Trade_8K9XkLaA',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-23 15:00:00',\n",
       "  '600588',\n",
       "  17.07,\n",
       "  1000.0,\n",
       "  'Order_ilQ9YC0q',\n",
       "  'Trade_ZXnPuwI0',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-23 09:30:00',\n",
       "  '600797',\n",
       "  12.25,\n",
       "  -1000.0,\n",
       "  'Order_QPnpyLW4',\n",
       "  'Trade_b1ZSzCWc',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  18.375],\n",
       " ['2018-01-24 15:00:00',\n",
       "  '000066',\n",
       "  7.16,\n",
       "  1000.0,\n",
       "  'Order_HFMEpgOP',\n",
       "  'Trade_2wMHE9LF',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-24 15:00:00',\n",
       "  '000836',\n",
       "  5.28,\n",
       "  1000.0,\n",
       "  'Order_pFjkchBw',\n",
       "  'Trade_QV2wqHpP',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-24 15:00:00',\n",
       "  '300036',\n",
       "  15.46,\n",
       "  1000.0,\n",
       "  'Order_HtPTd1qp',\n",
       "  'Trade_IzNvwty6',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-26 15:00:00',\n",
       "  '600289',\n",
       "  5.04,\n",
       "  1000.0,\n",
       "  'Order_aYKl4ptT',\n",
       "  'Trade_NyuYX6Uv',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-29 09:30:00',\n",
       "  '300366',\n",
       "  11.38,\n",
       "  -1000.0,\n",
       "  'Order_riElTH1g',\n",
       "  'Trade_JzLhNQWj',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  17.069999999999997],\n",
       " ['2018-01-29 15:00:00',\n",
       "  '600410',\n",
       "  9.99,\n",
       "  1000.0,\n",
       "  'Order_pARN0Vm5',\n",
       "  'Trade_oftUNq7i',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-30 09:30:00',\n",
       "  '300245',\n",
       "  13.32,\n",
       "  -1000.0,\n",
       "  'Order_hDKHk3iM',\n",
       "  'Trade_HZFeDEGt',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  19.987499999999997],\n",
       " ['2018-01-31 09:30:00',\n",
       "  '000070',\n",
       "  9.45,\n",
       "  -1000.0,\n",
       "  'Order_6VvYzxeS',\n",
       "  'Trade_nLHz0NkE',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  14.174999999999999],\n",
       " ['2018-01-31 09:30:00',\n",
       "  '002065',\n",
       "  7.99,\n",
       "  -1000.0,\n",
       "  'Order_CPuaDKTU',\n",
       "  'Trade_U1rElcD4',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  11.985000000000001],\n",
       " ['2018-01-31 09:30:00',\n",
       "  '002195',\n",
       "  6.16,\n",
       "  -1000.0,\n",
       "  'Order_XeWoOhbL',\n",
       "  'Trade_6qNlytIj',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  9.247499999999999],\n",
       " ['2018-01-31 15:00:00',\n",
       "  '002396',\n",
       "  19.67,\n",
       "  1000.0,\n",
       "  'Order_Xx9JAlEn',\n",
       "  'Trade_uwFUMzVr',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-01-31 09:30:00',\n",
       "  '600410',\n",
       "  9.72,\n",
       "  -1000.0,\n",
       "  'Order_czhPeW4i',\n",
       "  'Trade_eXhdrtKB',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  14.5875],\n",
       " ['2018-02-01 09:30:00',\n",
       "  '000066',\n",
       "  6.9,\n",
       "  -1000.0,\n",
       "  'Order_KTn78WLA',\n",
       "  'Trade_BGFMYArv',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  10.350000000000001],\n",
       " ['2018-02-01 09:30:00',\n",
       "  '002396',\n",
       "  19.21,\n",
       "  -1000.0,\n",
       "  'Order_k4BrM7gO',\n",
       "  'Trade_wgViXO7F',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  28.8225],\n",
       " ['2018-02-01 09:30:00',\n",
       "  '300036',\n",
       "  14.16,\n",
       "  -1000.0,\n",
       "  'Order_1KDhJsIU',\n",
       "  'Trade_mwBhVlnc',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  21.240209845050003],\n",
       " ['2018-02-01 09:30:00',\n",
       "  '300297',\n",
       "  9.22,\n",
       "  -1000.0,\n",
       "  'Order_VpJkeclr',\n",
       "  'Trade_2CzdBYlF',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  13.837499999999999],\n",
       " ['2018-02-01 09:30:00',\n",
       "  '600105',\n",
       "  6.15,\n",
       "  -1000.0,\n",
       "  'Order_THm0eubU',\n",
       "  'Trade_PDmalHWE',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  9.2325],\n",
       " ['2018-02-01 09:30:00',\n",
       "  '600804',\n",
       "  16.0,\n",
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       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-04-26 15:00:00',\n",
       "  '002063',\n",
       "  11.72,\n",
       "  1000.0,\n",
       "  'Order_t0QnK9dD',\n",
       "  'Trade_IZB8cFp9',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  0.0],\n",
       " ['2018-04-26 15:00:00',\n",
       "  '002544',\n",
       "  18.22,\n",
       "  1000.0,\n",
       "  'Order_pxRVawKn',\n",
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       "  5.0,\n",
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       " ['2018-04-26 09:30:00',\n",
       "  '300297',\n",
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       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  15.960000000000003],\n",
       " ['2018-04-26 09:30:00',\n",
       "  '600385',\n",
       "  12.39,\n",
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       "  'Order_fNnz7dXv',\n",
       "  'Trade_N9Ve3h2U',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  18.585],\n",
       " ['2018-04-27 09:30:00',\n",
       "  '002063',\n",
       "  11.64,\n",
       "  -1000.0,\n",
       "  'Order_fJHRcuAB',\n",
       "  'Trade_QcYru5DV',\n",
       "  'JCSC_EXAMPLE',\n",
       "  5.0,\n",
       "  17.4675],\n",
       " ['2018-04-27 09:30:00',\n",
       "  '002368',\n",
       "  31.73,\n",
       "  -1000.0,\n",
       "  'Order_dy701SjR',\n",
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       "  'JCSC_EXAMPLE',\n",
       "  7.9325,\n",
       "  47.595000000000006]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>code</th>\n",
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       "      <th>0</th>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
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       "      <td>1000.0</td>\n",
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       "      <td>5.000000</td>\n",
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       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>002065</td>\n",
       "      <td>8.53</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_5MvgxTLt</td>\n",
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       "      <td>2018-01-03 15:00:00</td>\n",
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       "      <td>1000.0</td>\n",
       "      <td>Order_cKI5HDl8</td>\n",
       "      <td>Trade_omAkLOGx</td>\n",
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       "      <td>5.000000</td>\n",
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       "      <td>2018-01-03 15:00:00</td>\n",
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       "      <td>1000.0</td>\n",
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       "      <th>9</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
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       "      <td>1000.0</td>\n",
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       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
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       "      <th>10</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>600804</td>\n",
       "      <td>17.93</td>\n",
       "      <td>1000.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>19.95</td>\n",
       "      <td>1000.0</td>\n",
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       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>603019</td>\n",
       "      <td>42.53</td>\n",
       "      <td>1000.0</td>\n",
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       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>2018-01-05 15:00:00</td>\n",
       "      <td>300051</td>\n",
       "      <td>9.80</td>\n",
       "      <td>1000.0</td>\n",
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       "      <td>Trade_MZ4NOVE5</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-01-10 09:30:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>19.23</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_XRiYpusq</td>\n",
       "      <td>Trade_xY9P4eGr</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-01-11 15:00:00</td>\n",
       "      <td>002197</td>\n",
       "      <td>12.82</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_pkabzXw9</td>\n",
       "      <td>Trade_Qk7bjUyo</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-01-11 15:00:00</td>\n",
       "      <td>600601</td>\n",
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       "      <td>1000.0</td>\n",
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       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>002197</td>\n",
       "      <td>12.06</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_wKERgClG</td>\n",
       "      <td>Trade_E3UtcdWy</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.090000</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>300036</td>\n",
       "      <td>14.49</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_frBKjRAa</td>\n",
       "      <td>Trade_lahUpuOV</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.732953</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>300290</td>\n",
       "      <td>8.08</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_xS0pjWz9</td>\n",
       "      <td>Trade_TSf9Mva1</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>600198</td>\n",
       "      <td>10.61</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_YvLncXNH</td>\n",
       "      <td>Trade_6fX9pcoH</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.915000</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.59</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_1q2mNkKh</td>\n",
       "      <td>Trade_PtGpJWZF</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.392500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>603019</td>\n",
       "      <td>37.48</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_An5xod2g</td>\n",
       "      <td>Trade_D1YwqSTA</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>9.369612</td>\n",
       "      <td>56.217673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>002544</td>\n",
       "      <td>14.62</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_p5FaMcRr</td>\n",
       "      <td>Trade_krqRKVn9</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>600718</td>\n",
       "      <td>14.27</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_P6RCfBtL</td>\n",
       "      <td>Trade_xvaCt3W1</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.397500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>600804</td>\n",
       "      <td>16.51</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_wo9xpg6j</td>\n",
       "      <td>Trade_dCM4bzyl</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-01-17 15:00:00</td>\n",
       "      <td>300297</td>\n",
       "      <td>9.56</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_oEip0DhY</td>\n",
       "      <td>Trade_grtUPdf5</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-01-19 09:30:00</td>\n",
       "      <td>300051</td>\n",
       "      <td>9.81</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_ljt8XCM9</td>\n",
       "      <td>Trade_KnYN75Za</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.715000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-01-19 15:00:00</td>\n",
       "      <td>600804</td>\n",
       "      <td>17.22</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_NiV4FmYK</td>\n",
       "      <td>Trade_L3CkFK0d</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-01-22 15:00:00</td>\n",
       "      <td>002095</td>\n",
       "      <td>38.61</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_PeK5RBIJ</td>\n",
       "      <td>Trade_VmH1B8M2</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>9.652500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2018-04-12 15:00:00</td>\n",
       "      <td>600385</td>\n",
       "      <td>13.10</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_DaA5uWBI</td>\n",
       "      <td>Trade_Jh2Yyaoj</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>002642</td>\n",
       "      <td>14.01</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_UfgBIH1d</td>\n",
       "      <td>Trade_MZ7HlR95</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>600589</td>\n",
       "      <td>5.47</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_PlNE1uoq</td>\n",
       "      <td>Trade_qm8PUXYf</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2018-04-13 09:30:00</td>\n",
       "      <td>600756</td>\n",
       "      <td>17.87</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_Kb1LZd4I</td>\n",
       "      <td>Trade_jF8pc9MT</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>26.805000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>18.80</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_8lEuY9Fx</td>\n",
       "      <td>Trade_TpowHvLg</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>300036</td>\n",
       "      <td>19.69</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_YEGLKufJ</td>\n",
       "      <td>Trade_v9C2ecja</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.534717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>600589</td>\n",
       "      <td>5.37</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_3V1sMokx</td>\n",
       "      <td>Trade_5E6KvGgB</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.055000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>600770</td>\n",
       "      <td>7.12</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_YuKcNoaP</td>\n",
       "      <td>Trade_me8O7IqZ</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>18.67</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_XjxTODLM</td>\n",
       "      <td>Trade_t4l318gn</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>300051</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_lEtzAXRO</td>\n",
       "      <td>Trade_LyQufab4</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>600100</td>\n",
       "      <td>10.74</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_tzINe2Bj</td>\n",
       "      <td>Trade_5Vw9AZOo</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>600770</td>\n",
       "      <td>7.51</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_EZkhLb0S</td>\n",
       "      <td>Trade_1YN7C6Eb</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>19.60</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_pSly4RNJ</td>\n",
       "      <td>Trade_tFhPLQZN</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2018-04-20 09:30:00</td>\n",
       "      <td>300051</td>\n",
       "      <td>11.29</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_NDpZB6QW</td>\n",
       "      <td>Trade_K5MdlmCD</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.942500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2018-04-20 15:00:00</td>\n",
       "      <td>600589</td>\n",
       "      <td>5.47</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_aLtHfScz</td>\n",
       "      <td>Trade_xSaQMBdK</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2018-04-20 15:00:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_aNRh3ew4</td>\n",
       "      <td>Trade_pb1gKVjk</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2018-04-23 09:30:00</td>\n",
       "      <td>002642</td>\n",
       "      <td>12.42</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_Mc6zbLCw</td>\n",
       "      <td>Trade_QI1xJK5v</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2018-04-23 15:00:00</td>\n",
       "      <td>300366</td>\n",
       "      <td>12.45</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_Tew5CWFN</td>\n",
       "      <td>Trade_8Lb4Hckx</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2018-04-23 09:30:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.08</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_b4Jwn1i9</td>\n",
       "      <td>Trade_EfSl5VjW</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.612500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2018-04-24 15:00:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_OtZfEe9b</td>\n",
       "      <td>Trade_tBv9PxTf</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2018-04-25 09:30:00</td>\n",
       "      <td>600845</td>\n",
       "      <td>30.00</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_BbZAmTPz</td>\n",
       "      <td>Trade_msU4CXab</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>7.500343</td>\n",
       "      <td>45.002060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>000066</td>\n",
       "      <td>9.28</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_cY47nJdk</td>\n",
       "      <td>Trade_tORLb2G0</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>000611</td>\n",
       "      <td>7.56</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_GH1Rqkt9</td>\n",
       "      <td>Trade_a6GsBcIx</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>000836</td>\n",
       "      <td>4.56</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_GKWiYO3R</td>\n",
       "      <td>Trade_WVfrl0U4</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>002063</td>\n",
       "      <td>11.72</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_t0QnK9dD</td>\n",
       "      <td>Trade_IZB8cFp9</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>002544</td>\n",
       "      <td>18.22</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_pxRVawKn</td>\n",
       "      <td>Trade_VFA5vRr9</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>300297</td>\n",
       "      <td>10.64</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_X8e607YD</td>\n",
       "      <td>Trade_JKFDN3vX</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>600385</td>\n",
       "      <td>12.39</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_fNnz7dXv</td>\n",
       "      <td>Trade_N9Ve3h2U</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2018-04-27 09:30:00</td>\n",
       "      <td>002063</td>\n",
       "      <td>11.64</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_fJHRcuAB</td>\n",
       "      <td>Trade_QcYru5DV</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.467500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2018-04-27 09:30:00</td>\n",
       "      <td>002368</td>\n",
       "      <td>31.73</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_dy701SjR</td>\n",
       "      <td>Trade_5LgrYZT8</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>7.932500</td>\n",
       "      <td>47.595000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                datetime    code  price  amount        order_id  \\\n",
       "0    2018-01-02 15:00:00  002195   5.91  1000.0  Order_dRhVu53P   \n",
       "1    2018-01-02 15:00:00  002544  15.85  1000.0  Order_ykw3N6uH   \n",
       "2    2018-01-02 15:00:00  300290   8.71  1000.0  Order_FfO0nlsc   \n",
       "3    2018-01-02 15:00:00  600105   6.58  1000.0  Order_UvYiHIK1   \n",
       "4    2018-01-02 15:00:00  600797  11.87  1000.0  Order_yGimNbhn   \n",
       "5    2018-01-03 15:00:00  000070   9.52  1000.0  Order_l97R514S   \n",
       "6    2018-01-03 15:00:00  002065   8.53  1000.0  Order_5MvgxTLt   \n",
       "7    2018-01-03 15:00:00  300036  15.78  1000.0  Order_cKI5HDl8   \n",
       "8    2018-01-03 15:00:00  600198  11.65  1000.0  Order_bTHhagWP   \n",
       "9    2018-01-03 15:00:00  600718  15.00  1000.0  Order_hHLinCrl   \n",
       "10   2018-01-03 15:00:00  600804  17.93  1000.0  Order_4uv6fj8Q   \n",
       "11   2018-01-03 15:00:00  600850  19.95  1000.0  Order_hCyLzeHQ   \n",
       "12   2018-01-03 15:00:00  603019  42.53  1000.0  Order_U3RVGrve   \n",
       "13   2018-01-05 15:00:00  300051   9.80  1000.0  Order_9qoJpiQZ   \n",
       "14   2018-01-10 09:30:00  600850  19.23 -1000.0  Order_XRiYpusq   \n",
       "15   2018-01-11 15:00:00  002197  12.82  1000.0  Order_pkabzXw9   \n",
       "16   2018-01-11 15:00:00  600601   3.69  1000.0  Order_tqDujrOf   \n",
       "17   2018-01-15 09:30:00  002197  12.06 -1000.0  Order_wKERgClG   \n",
       "18   2018-01-15 09:30:00  300036  14.49 -1000.0  Order_frBKjRAa   \n",
       "19   2018-01-15 09:30:00  300290   8.08 -1000.0  Order_xS0pjWz9   \n",
       "20   2018-01-15 09:30:00  600198  10.61 -1000.0  Order_YvLncXNH   \n",
       "21   2018-01-15 09:30:00  600601   3.59 -1000.0  Order_1q2mNkKh   \n",
       "22   2018-01-15 09:30:00  603019  37.48 -1000.0  Order_An5xod2g   \n",
       "23   2018-01-16 09:30:00  002544  14.62 -1000.0  Order_p5FaMcRr   \n",
       "24   2018-01-16 09:30:00  600718  14.27 -1000.0  Order_P6RCfBtL   \n",
       "25   2018-01-16 09:30:00  600804  16.51 -1000.0  Order_wo9xpg6j   \n",
       "26   2018-01-17 15:00:00  300297   9.56  1000.0  Order_oEip0DhY   \n",
       "27   2018-01-19 09:30:00  300051   9.81 -1000.0  Order_ljt8XCM9   \n",
       "28   2018-01-19 15:00:00  600804  17.22  1000.0  Order_NiV4FmYK   \n",
       "29   2018-01-22 15:00:00  002095  38.61  1000.0  Order_PeK5RBIJ   \n",
       "..                   ...     ...    ...     ...             ...   \n",
       "105  2018-04-12 15:00:00  600385  13.10  1000.0  Order_DaA5uWBI   \n",
       "106  2018-04-13 15:00:00  002642  14.01  1000.0  Order_UfgBIH1d   \n",
       "107  2018-04-13 15:00:00  600589   5.47  1000.0  Order_PlNE1uoq   \n",
       "108  2018-04-13 09:30:00  600756  17.87 -1000.0  Order_Kb1LZd4I   \n",
       "109  2018-04-13 15:00:00  600850  18.80  1000.0  Order_8lEuY9Fx   \n",
       "110  2018-04-17 09:30:00  300036  19.69 -1000.0  Order_YEGLKufJ   \n",
       "111  2018-04-17 09:30:00  600589   5.37 -1000.0  Order_3V1sMokx   \n",
       "112  2018-04-17 09:30:00  600770   7.12 -1000.0  Order_YuKcNoaP   \n",
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       "116  2018-04-18 15:00:00  600770   7.51  1000.0  Order_EZkhLb0S   \n",
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       "118  2018-04-20 09:30:00  300051  11.29 -1000.0  Order_NDpZB6QW   \n",
       "119  2018-04-20 15:00:00  600589   5.47  1000.0  Order_aLtHfScz   \n",
       "120  2018-04-20 15:00:00  600601   3.09  1000.0  Order_aNRh3ew4   \n",
       "121  2018-04-23 09:30:00  002642  12.42 -1000.0  Order_Mc6zbLCw   \n",
       "122  2018-04-23 15:00:00  300366  12.45  1000.0  Order_Tew5CWFN   \n",
       "123  2018-04-23 09:30:00  600601   3.08 -1000.0  Order_b4Jwn1i9   \n",
       "124  2018-04-24 15:00:00  600601   3.08  1000.0  Order_OtZfEe9b   \n",
       "125  2018-04-25 09:30:00  600845  30.00 -1000.0  Order_BbZAmTPz   \n",
       "126  2018-04-26 09:30:00  000066   9.28 -1000.0  Order_cY47nJdk   \n",
       "127  2018-04-26 15:00:00  000611   7.56  1000.0  Order_GH1Rqkt9   \n",
       "128  2018-04-26 15:00:00  000836   4.56  1000.0  Order_GKWiYO3R   \n",
       "129  2018-04-26 15:00:00  002063  11.72  1000.0  Order_t0QnK9dD   \n",
       "130  2018-04-26 15:00:00  002544  18.22  1000.0  Order_pxRVawKn   \n",
       "131  2018-04-26 09:30:00  300297  10.64 -1000.0  Order_X8e607YD   \n",
       "132  2018-04-26 09:30:00  600385  12.39 -1000.0  Order_fNnz7dXv   \n",
       "133  2018-04-27 09:30:00  002063  11.64 -1000.0  Order_fJHRcuAB   \n",
       "134  2018-04-27 09:30:00  002368  31.73 -1000.0  Order_dy701SjR   \n",
       "\n",
       "           trade_id account_cookie  commission        tax  \n",
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       "7    Trade_omAkLOGx   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "8    Trade_AHe9zjXL   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "9    Trade_gGVZT4uc   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "10   Trade_FvJX1o0r   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "11   Trade_Dlg8ahqj   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "12   Trade_okmF7CPM   JCSC_EXAMPLE   10.632202   0.000000  \n",
       "13   Trade_MZ4NOVE5   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "14   Trade_xY9P4eGr   JCSC_EXAMPLE    5.000000  28.852500  \n",
       "15   Trade_Qk7bjUyo   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "16   Trade_CizLsXG8   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "17   Trade_E3UtcdWy   JCSC_EXAMPLE    5.000000  18.090000  \n",
       "18   Trade_lahUpuOV   JCSC_EXAMPLE    5.000000  21.732953  \n",
       "19   Trade_TSf9Mva1   JCSC_EXAMPLE    5.000000  12.120000  \n",
       "20   Trade_6fX9pcoH   JCSC_EXAMPLE    5.000000  15.915000  \n",
       "21   Trade_PtGpJWZF   JCSC_EXAMPLE    5.000000   5.392500  \n",
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       "25   Trade_dCM4bzyl   JCSC_EXAMPLE    5.000000  24.765000  \n",
       "26   Trade_grtUPdf5   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "27   Trade_KnYN75Za   JCSC_EXAMPLE    5.000000  14.715000  \n",
       "28   Trade_L3CkFK0d   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "29   Trade_VmH1B8M2   JCSC_EXAMPLE    9.652500   0.000000  \n",
       "..              ...            ...         ...        ...  \n",
       "105  Trade_Jh2Yyaoj   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "106  Trade_MZ7HlR95   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "107  Trade_qm8PUXYf   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "108  Trade_jF8pc9MT   JCSC_EXAMPLE    5.000000  26.805000  \n",
       "109  Trade_TpowHvLg   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "110  Trade_v9C2ecja   JCSC_EXAMPLE    5.000000  29.534717  \n",
       "111  Trade_5E6KvGgB   JCSC_EXAMPLE    5.000000   8.055000  \n",
       "112  Trade_me8O7IqZ   JCSC_EXAMPLE    5.000000  10.687500  \n",
       "113  Trade_t4l318gn   JCSC_EXAMPLE    5.000000  28.005000  \n",
       "114  Trade_LyQufab4   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "115  Trade_5Vw9AZOo   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "116  Trade_1YN7C6Eb   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "117  Trade_tFhPLQZN   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "118  Trade_K5MdlmCD   JCSC_EXAMPLE    5.000000  16.942500  \n",
       "119  Trade_xSaQMBdK   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "120  Trade_pb1gKVjk   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "121  Trade_QI1xJK5v   JCSC_EXAMPLE    5.000000  18.630000  \n",
       "122  Trade_8Lb4Hckx   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "123  Trade_EfSl5VjW   JCSC_EXAMPLE    5.000000   4.612500  \n",
       "124  Trade_tBv9PxTf   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "125  Trade_msU4CXab   JCSC_EXAMPLE    7.500343  45.002060  \n",
       "126  Trade_tORLb2G0   JCSC_EXAMPLE    5.000000  13.920000  \n",
       "127  Trade_a6GsBcIx   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "128  Trade_WVfrl0U4   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "129  Trade_IZB8cFp9   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "130  Trade_VFA5vRr9   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "131  Trade_JKFDN3vX   JCSC_EXAMPLE    5.000000  15.960000  \n",
       "132  Trade_N9Ve3h2U   JCSC_EXAMPLE    5.000000  18.585000  \n",
       "133  Trade_QcYru5DV   JCSC_EXAMPLE    5.000000  17.467500  \n",
       "134  Trade_5LgrYZT8   JCSC_EXAMPLE    7.932500  47.595000  \n",
       "\n",
       "[135 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.history_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>code</th>\n",
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       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-23</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-14</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-15</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-16</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-19</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-20</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-21</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-22</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-23</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-26</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-28</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-04</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-09</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-10</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-11</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-12</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-13</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-17</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-18</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-20</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-27</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "code                       000066  000070  000611  000836  000977  002063  \\\n",
       "date       account_cookie                                                   \n",
       "2018-01-02 JCSC_EXAMPLE       0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-03 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-05 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-10 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-11 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-15 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-16 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-17 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-19 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-22 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-23 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0     0.0     0.0   \n",
       "2018-01-24 JCSC_EXAMPLE    1000.0  1000.0     0.0  1000.0     0.0     0.0   \n",
       "2018-01-26 JCSC_EXAMPLE    1000.0  1000.0     0.0  1000.0     0.0     0.0   \n",
       "2018-01-29 JCSC_EXAMPLE    1000.0  1000.0     0.0  1000.0     0.0     0.0   \n",
       "2018-01-30 JCSC_EXAMPLE    1000.0  1000.0     0.0  1000.0     0.0     0.0   \n",
       "2018-01-31 JCSC_EXAMPLE    1000.0     0.0     0.0  1000.0     0.0     0.0   \n",
       "2018-02-01 JCSC_EXAMPLE       0.0     0.0     0.0  1000.0     0.0     0.0   \n",
       "2018-02-06 JCSC_EXAMPLE       0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-02-12 JCSC_EXAMPLE       0.0     0.0     0.0     0.0  1000.0     0.0   \n",
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       "code                       002065  002095  002195  002197   ...    600601  \\\n",
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       "\n",
       "code                       600718  600756  600770  600797  600804  600845  \\\n",
       "date       account_cookie                                                   \n",
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       "2018-03-27 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0  1000.0     0.0   \n",
       "2018-03-28 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0  1000.0     0.0   \n",
       "2018-03-29 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0  1000.0     0.0   \n",
       "2018-04-02 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0  1000.0     0.0   \n",
       "2018-04-04 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0  1000.0     0.0   \n",
       "2018-04-09 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0  1000.0     0.0   \n",
       "2018-04-10 JCSC_EXAMPLE       0.0  1000.0     0.0     0.0  1000.0     0.0   \n",
       "2018-04-11 JCSC_EXAMPLE       0.0  1000.0  1000.0     0.0  1000.0  1000.0   \n",
       "2018-04-12 JCSC_EXAMPLE       0.0  1000.0  1000.0     0.0  1000.0  1000.0   \n",
       "2018-04-13 JCSC_EXAMPLE       0.0     0.0  1000.0     0.0  1000.0  1000.0   \n",
       "2018-04-17 JCSC_EXAMPLE       0.0     0.0     0.0     0.0  1000.0  1000.0   \n",
       "2018-04-18 JCSC_EXAMPLE       0.0     0.0  1000.0     0.0  1000.0  1000.0   \n",
       "2018-04-20 JCSC_EXAMPLE       0.0     0.0  1000.0     0.0  1000.0  1000.0   \n",
       "2018-04-23 JCSC_EXAMPLE       0.0     0.0  1000.0     0.0  1000.0  1000.0   \n",
       "2018-04-24 JCSC_EXAMPLE       0.0     0.0  1000.0     0.0  1000.0  1000.0   \n",
       "2018-04-25 JCSC_EXAMPLE       0.0     0.0  1000.0     0.0  1000.0     0.0   \n",
       "2018-04-26 JCSC_EXAMPLE       0.0     0.0  1000.0     0.0  1000.0     0.0   \n",
       "2018-04-27 JCSC_EXAMPLE       0.0     0.0  1000.0     0.0  1000.0     0.0   \n",
       "\n",
       "code                       600850  603019  603138  \n",
       "date       account_cookie                          \n",
       "2018-01-02 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-01-03 JCSC_EXAMPLE    1000.0  1000.0     0.0  \n",
       "2018-01-05 JCSC_EXAMPLE    1000.0  1000.0     0.0  \n",
       "2018-01-10 JCSC_EXAMPLE       0.0  1000.0     0.0  \n",
       "2018-01-11 JCSC_EXAMPLE       0.0  1000.0     0.0  \n",
       "2018-01-15 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-01-16 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-01-17 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-01-19 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-01-22 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
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       "2018-01-26 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-01-29 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-01-30 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
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       "2018-02-06 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-02-12 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-02-13 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-02-14 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-02-22 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-02-23 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
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       "2018-03-15 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-03-16 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-03-19 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
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       "2018-03-22 JCSC_EXAMPLE       0.0     0.0  1000.0  \n",
       "2018-03-23 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-03-26 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-03-27 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-03-28 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-03-29 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-04-02 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-04-04 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-04-09 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-04-10 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-04-11 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-04-12 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-04-13 JCSC_EXAMPLE    1000.0     0.0     0.0  \n",
       "2018-04-17 JCSC_EXAMPLE       0.0     0.0     0.0  \n",
       "2018-04-18 JCSC_EXAMPLE    1000.0     0.0     0.0  \n",
       "2018-04-20 JCSC_EXAMPLE    1000.0     0.0     0.0  \n",
       "2018-04-23 JCSC_EXAMPLE    1000.0     0.0     0.0  \n",
       "2018-04-24 JCSC_EXAMPLE    1000.0     0.0     0.0  \n",
       "2018-04-25 JCSC_EXAMPLE    1000.0     0.0     0.0  \n",
       "2018-04-26 JCSC_EXAMPLE    1000.0     0.0     0.0  \n",
       "2018-04-27 JCSC_EXAMPLE    1000.0     0.0     0.0  \n",
       "\n",
       "[50 rows x 43 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.daily_hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\quantaxis\\QUANTAXIS\\QAData\\QADataStruct.py:114: FutureWarning: 'code' is both an index level and a column label.\n",
      "Defaulting to column, but this will raise an ambiguity error in a future version\n",
      "  self.data.groupby('code').apply(QA_data_stock_to_fq), self.type, 'qfq')\n"
     ]
    }
   ],
   "source": [
    "Risk=QA.QA_Risk(Account)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'account_cookie': 'JCSC_EXAMPLE',\n",
       " 'portfolio_cookie': None,\n",
       " 'user_cookie': None,\n",
       " 'annualize_return': 0.07,\n",
       " 'profit': 0.02,\n",
       " 'max_dropback': 0.12,\n",
       " 'time_gap': 77,\n",
       " 'volatility': 0.53,\n",
       " 'benchmark_code': '000300',\n",
       " 'bm_annualizereturn': -0.26,\n",
       " 'bn_profit': -0.07,\n",
       " 'beta': 1.0,\n",
       " 'alpha': 0.33,\n",
       " 'sharpe': 0.04,\n",
       " 'init_cash': '200000.00',\n",
       " 'last_assets': '204497.60'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code\n",
       "000066        0.0\n",
       "000070        0.0\n",
       "000611      -10.0\n",
       "000836     -230.0\n",
       "000977        0.0\n",
       "002063   -11720.0\n",
       "002065        0.0\n",
       "002095        0.0\n",
       "002195        0.0\n",
       "002197        0.0\n",
       "002268        0.0\n",
       "002368   -33000.0\n",
       "002396        0.0\n",
       "002544      180.0\n",
       "002642        0.0\n",
       "300036        0.0\n",
       "300051        0.0\n",
       "300188        0.0\n",
       "300229        NaN\n",
       "300245       10.0\n",
       "300290        NaN\n",
       "300297        0.0\n",
       "300366       10.0\n",
       "600100      -60.0\n",
       "600105        0.0\n",
       "600198        NaN\n",
       "600289      170.0\n",
       "600385        0.0\n",
       "600410        0.0\n",
       "600522        0.0\n",
       "600588        0.0\n",
       "600589      140.0\n",
       "600595       30.0\n",
       "600601     -310.0\n",
       "600718        0.0\n",
       "600756        0.0\n",
       "600770     -380.0\n",
       "600797        0.0\n",
       "600804      -10.0\n",
       "600845        0.0\n",
       "600850       70.0\n",
       "603019        0.0\n",
       "603138        0.0\n",
       "Name: (2018-04-27 00:00:00, JCSC_EXAMPLE), dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.market_value.diff().iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <th>JCSC_EXAMPLE</th>\n",
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       "      <td>2018-01-10</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "    <tr>\n",
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       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>2018-01-16</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>2018-01-19</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <th>JCSC_EXAMPLE</th>\n",
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       "      <td>2018-01-19</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "    <tr>\n",
       "      <th>2018-01-22 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
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       "      <td>2018-01-22 15:00:00</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-12 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>21169.8</td>\n",
       "      <td>2018-04-12 15:00:00</td>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-04-13 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
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       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1679.84</td>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-13 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>19518</td>\n",
       "      <td>2018-04-13 09:30:00</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-13 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>713.034</td>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2018-04-17 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>20368.5</td>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>25725.4</td>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>32829.8</td>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>51466.8</td>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2018-04-18 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>39461.8</td>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>28716.8</td>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>21201.8</td>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1596.75</td>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-20 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>12864.8</td>\n",
       "      <td>2018-04-20 09:30:00</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-04-20 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>7389.81</td>\n",
       "      <td>2018-04-20 15:00:00</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>4294.81</td>\n",
       "      <td>2018-04-20 15:00:00</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>16691.2</td>\n",
       "      <td>2018-04-23 09:30:00</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>4236.18</td>\n",
       "      <td>2018-04-23 15:00:00</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>7306.57</td>\n",
       "      <td>2018-04-23 09:30:00</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>4221.57</td>\n",
       "      <td>2018-04-24 15:00:00</td>\n",
       "      <td>2018-04-24</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>34169.1</td>\n",
       "      <td>2018-04-25 09:30:00</td>\n",
       "      <td>2018-04-25</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>43430.1</td>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2018-04-26 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>35865.1</td>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>31300.1</td>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>19575.1</td>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
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       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-04-26 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>11969.2</td>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>24335.6</td>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-04-27 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>35953.1</td>\n",
       "      <td>2018-04-27 09:30:00</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>67627.6</td>\n",
       "      <td>2018-04-27 09:30:00</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       cash             datetime       date  \\\n",
       "datetime            account_cookie                                            \n",
       "2018-01-02 15:00:00 JCSC_EXAMPLE     194085  2018-01-02 15:00:00 2018-01-02   \n",
       "                    JCSC_EXAMPLE     178230  2018-01-02 15:00:00 2018-01-02   \n",
       "                    JCSC_EXAMPLE     169515  2018-01-02 15:00:00 2018-01-02   \n",
       "                    JCSC_EXAMPLE     162930  2018-01-02 15:00:00 2018-01-02   \n",
       "                    JCSC_EXAMPLE     151055  2018-01-02 15:00:00 2018-01-02   \n",
       "2018-01-03 15:00:00 JCSC_EXAMPLE     141530  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE     132995  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE     117210  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE     105555  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE      90550  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE      72615  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE      52660  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE    10119.4  2018-01-03 15:00:00 2018-01-03   \n",
       "2018-01-05 15:00:00 JCSC_EXAMPLE    314.368  2018-01-05 15:00:00 2018-01-05   \n",
       "2018-01-10 09:30:00 JCSC_EXAMPLE    19510.5  2018-01-10 09:30:00 2018-01-10   \n",
       "2018-01-11 15:00:00 JCSC_EXAMPLE    6685.52  2018-01-11 15:00:00 2018-01-11   \n",
       "                    JCSC_EXAMPLE    2990.52  2018-01-11 15:00:00 2018-01-11   \n",
       "2018-01-15 09:30:00 JCSC_EXAMPLE    15027.4  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    29490.7  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    37553.6  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    48142.7  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    51722.3  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    89136.7  2018-01-15 09:30:00 2018-01-15   \n",
       "2018-01-16 09:30:00 JCSC_EXAMPLE     103730  2018-01-16 09:30:00 2018-01-16   \n",
       "                    JCSC_EXAMPLE     117973  2018-01-16 09:30:00 2018-01-16   \n",
       "                    JCSC_EXAMPLE     134454  2018-01-16 09:30:00 2018-01-16   \n",
       "2018-01-17 15:00:00 JCSC_EXAMPLE     124889  2018-01-17 15:00:00 2018-01-17   \n",
       "2018-01-19 09:30:00 JCSC_EXAMPLE     134679  2018-01-19 09:30:00 2018-01-19   \n",
       "2018-01-19 15:00:00 JCSC_EXAMPLE     117454  2018-01-19 15:00:00 2018-01-19   \n",
       "2018-01-22 15:00:00 JCSC_EXAMPLE    78834.2  2018-01-22 15:00:00 2018-01-22   \n",
       "...                                     ...                  ...        ...   \n",
       "2018-04-12 15:00:00 JCSC_EXAMPLE    21169.8  2018-04-12 15:00:00 2018-04-12   \n",
       "2018-04-13 15:00:00 JCSC_EXAMPLE    7154.84  2018-04-13 15:00:00 2018-04-13   \n",
       "                    JCSC_EXAMPLE    1679.84  2018-04-13 15:00:00 2018-04-13   \n",
       "2018-04-13 09:30:00 JCSC_EXAMPLE      19518  2018-04-13 09:30:00 2018-04-13   \n",
       "2018-04-13 15:00:00 JCSC_EXAMPLE    713.034  2018-04-13 15:00:00 2018-04-13   \n",
       "2018-04-17 09:30:00 JCSC_EXAMPLE    20368.5  2018-04-17 09:30:00 2018-04-17   \n",
       "                    JCSC_EXAMPLE    25725.4  2018-04-17 09:30:00 2018-04-17   \n",
       "                    JCSC_EXAMPLE    32829.8  2018-04-17 09:30:00 2018-04-17   \n",
       "                    JCSC_EXAMPLE    51466.8  2018-04-17 09:30:00 2018-04-17   \n",
       "2018-04-18 15:00:00 JCSC_EXAMPLE    39461.8  2018-04-18 15:00:00 2018-04-18   \n",
       "                    JCSC_EXAMPLE    28716.8  2018-04-18 15:00:00 2018-04-18   \n",
       "                    JCSC_EXAMPLE    21201.8  2018-04-18 15:00:00 2018-04-18   \n",
       "                    JCSC_EXAMPLE    1596.75  2018-04-18 15:00:00 2018-04-18   \n",
       "2018-04-20 09:30:00 JCSC_EXAMPLE    12864.8  2018-04-20 09:30:00 2018-04-20   \n",
       "2018-04-20 15:00:00 JCSC_EXAMPLE    7389.81  2018-04-20 15:00:00 2018-04-20   \n",
       "                    JCSC_EXAMPLE    4294.81  2018-04-20 15:00:00 2018-04-20   \n",
       "2018-04-23 09:30:00 JCSC_EXAMPLE    16691.2  2018-04-23 09:30:00 2018-04-23   \n",
       "2018-04-23 15:00:00 JCSC_EXAMPLE    4236.18  2018-04-23 15:00:00 2018-04-23   \n",
       "2018-04-23 09:30:00 JCSC_EXAMPLE    7306.57  2018-04-23 09:30:00 2018-04-23   \n",
       "2018-04-24 15:00:00 JCSC_EXAMPLE    4221.57  2018-04-24 15:00:00 2018-04-24   \n",
       "2018-04-25 09:30:00 JCSC_EXAMPLE    34169.1  2018-04-25 09:30:00 2018-04-25   \n",
       "2018-04-26 09:30:00 JCSC_EXAMPLE    43430.1  2018-04-26 09:30:00 2018-04-26   \n",
       "2018-04-26 15:00:00 JCSC_EXAMPLE    35865.1  2018-04-26 15:00:00 2018-04-26   \n",
       "                    JCSC_EXAMPLE    31300.1  2018-04-26 15:00:00 2018-04-26   \n",
       "                    JCSC_EXAMPLE    19575.1  2018-04-26 15:00:00 2018-04-26   \n",
       "                    JCSC_EXAMPLE    1350.14  2018-04-26 15:00:00 2018-04-26   \n",
       "2018-04-26 09:30:00 JCSC_EXAMPLE    11969.2  2018-04-26 09:30:00 2018-04-26   \n",
       "                    JCSC_EXAMPLE    24335.6  2018-04-26 09:30:00 2018-04-26   \n",
       "2018-04-27 09:30:00 JCSC_EXAMPLE    35953.1  2018-04-27 09:30:00 2018-04-27   \n",
       "                    JCSC_EXAMPLE    67627.6  2018-04-27 09:30:00 2018-04-27   \n",
       "\n",
       "                                   account_cookie  \n",
       "datetime            account_cookie                 \n",
       "2018-01-02 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-03 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-05 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-10 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-11 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-15 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-16 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-17 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-19 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-19 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-22 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "...                                           ...  \n",
       "2018-04-12 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-13 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-13 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-13 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-17 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-18 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-20 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-20 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-23 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-23 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-23 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-24 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-25 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-26 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-26 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-26 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-27 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.account.cash_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        account_cookie\n",
       "2018-01-02  JCSC_EXAMPLE       48920.0\n",
       "2018-01-03  JCSC_EXAMPLE      190560.0\n",
       "2018-01-05  JCSC_EXAMPLE      190850.0\n",
       "2018-01-10  JCSC_EXAMPLE      168250.0\n",
       "2018-01-11  JCSC_EXAMPLE      199350.0\n",
       "2018-01-15  JCSC_EXAMPLE      100180.0\n",
       "2018-01-16  JCSC_EXAMPLE       55380.0\n",
       "2018-01-17  JCSC_EXAMPLE       63050.0\n",
       "2018-01-19  JCSC_EXAMPLE       70590.0\n",
       "2018-01-22  JCSC_EXAMPLE      124450.0\n",
       "2018-01-23  JCSC_EXAMPLE      160760.0\n",
       "2018-01-24  JCSC_EXAMPLE      194870.0\n",
       "2018-01-26  JCSC_EXAMPLE      197760.0\n",
       "2018-01-29  JCSC_EXAMPLE      191780.0\n",
       "2018-01-30  JCSC_EXAMPLE      179760.0\n",
       "2018-01-31  JCSC_EXAMPLE      160020.0\n",
       "2018-02-01  JCSC_EXAMPLE       86830.0\n",
       "2018-02-06  JCSC_EXAMPLE       27260.0\n",
       "2018-02-12  JCSC_EXAMPLE       89400.0\n",
       "2018-02-13  JCSC_EXAMPLE      144310.0\n",
       "2018-02-14  JCSC_EXAMPLE      159460.0\n",
       "2018-02-22  JCSC_EXAMPLE      188220.0\n",
       "2018-02-23  JCSC_EXAMPLE      190920.0\n",
       "2018-03-14  JCSC_EXAMPLE      194620.0\n",
       "2018-03-15  JCSC_EXAMPLE      158450.0\n",
       "2018-03-16  JCSC_EXAMPLE      171730.0\n",
       "2018-03-19  JCSC_EXAMPLE      148980.0\n",
       "2018-03-20  JCSC_EXAMPLE      117560.0\n",
       "2018-03-21  JCSC_EXAMPLE       79960.0\n",
       "2018-03-22  JCSC_EXAMPLE      122540.0\n",
       "2018-03-23  JCSC_EXAMPLE       51810.0\n",
       "2018-03-26  JCSC_EXAMPLE       92440.0\n",
       "2018-03-27  JCSC_EXAMPLE      180950.0\n",
       "2018-03-28  JCSC_EXAMPLE      196880.0\n",
       "2018-03-29  JCSC_EXAMPLE      209530.0\n",
       "2018-04-02  JCSC_EXAMPLE      219770.0\n",
       "2018-04-04  JCSC_EXAMPLE      207830.0\n",
       "2018-04-09  JCSC_EXAMPLE      195620.0\n",
       "2018-04-10  JCSC_EXAMPLE      170840.0\n",
       "2018-04-11  JCSC_EXAMPLE      213330.0\n",
       "2018-04-12  JCSC_EXAMPLE      191050.0\n",
       "2018-04-13  JCSC_EXAMPLE      214520.0\n",
       "2018-04-17  JCSC_EXAMPLE      151780.0\n",
       "2018-04-18  JCSC_EXAMPLE      210120.0\n",
       "2018-04-20  JCSC_EXAMPLE      204160.0\n",
       "2018-04-23  JCSC_EXAMPLE      194520.0\n",
       "2018-04-24  JCSC_EXAMPLE      204230.0\n",
       "2018-04-25  JCSC_EXAMPLE      171030.0\n",
       "2018-04-26  JCSC_EXAMPLE      173860.0\n",
       "2018-04-27  JCSC_EXAMPLE      136870.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.market_value.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>code</th>\n",
       "      <th>price</th>\n",
       "      <th>amount</th>\n",
       "      <th>order_id</th>\n",
       "      <th>trade_id</th>\n",
       "      <th>account_cookie</th>\n",
       "      <th>commission</th>\n",
       "      <th>tax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
       "      <td>002195</td>\n",
       "      <td>5.91</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_dRhVu53P</td>\n",
       "      <td>Trade_Xn1l6LPZ</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
       "      <td>002544</td>\n",
       "      <td>15.85</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_ykw3N6uH</td>\n",
       "      <td>Trade_cOpw7vmN</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
       "      <td>300290</td>\n",
       "      <td>8.71</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_FfO0nlsc</td>\n",
       "      <td>Trade_sZ61KjYP</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
       "      <td>600105</td>\n",
       "      <td>6.58</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_UvYiHIK1</td>\n",
       "      <td>Trade_lq6b7k9Z</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
       "      <td>600797</td>\n",
       "      <td>11.87</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_yGimNbhn</td>\n",
       "      <td>Trade_fPUCQ01t</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>000070</td>\n",
       "      <td>9.52</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_l97R514S</td>\n",
       "      <td>Trade_OhtnrxDC</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>002065</td>\n",
       "      <td>8.53</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_5MvgxTLt</td>\n",
       "      <td>Trade_rm7aYq5w</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>300036</td>\n",
       "      <td>15.78</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_cKI5HDl8</td>\n",
       "      <td>Trade_omAkLOGx</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>600198</td>\n",
       "      <td>11.65</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_bTHhagWP</td>\n",
       "      <td>Trade_AHe9zjXL</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>600718</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_hHLinCrl</td>\n",
       "      <td>Trade_gGVZT4uc</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>600804</td>\n",
       "      <td>17.93</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_4uv6fj8Q</td>\n",
       "      <td>Trade_FvJX1o0r</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>19.95</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_hCyLzeHQ</td>\n",
       "      <td>Trade_Dlg8ahqj</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>603019</td>\n",
       "      <td>42.53</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_U3RVGrve</td>\n",
       "      <td>Trade_okmF7CPM</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>10.632202</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2018-01-05 15:00:00</td>\n",
       "      <td>300051</td>\n",
       "      <td>9.80</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_9qoJpiQZ</td>\n",
       "      <td>Trade_MZ4NOVE5</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2018-01-10 09:30:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>19.23</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_XRiYpusq</td>\n",
       "      <td>Trade_xY9P4eGr</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28.852500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2018-01-11 15:00:00</td>\n",
       "      <td>002197</td>\n",
       "      <td>12.82</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_pkabzXw9</td>\n",
       "      <td>Trade_Qk7bjUyo</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018-01-11 15:00:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.69</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_tqDujrOf</td>\n",
       "      <td>Trade_CizLsXG8</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>002197</td>\n",
       "      <td>12.06</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_wKERgClG</td>\n",
       "      <td>Trade_E3UtcdWy</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.090000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>300036</td>\n",
       "      <td>14.49</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_frBKjRAa</td>\n",
       "      <td>Trade_lahUpuOV</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.732953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>300290</td>\n",
       "      <td>8.08</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_xS0pjWz9</td>\n",
       "      <td>Trade_TSf9Mva1</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>12.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>600198</td>\n",
       "      <td>10.61</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_YvLncXNH</td>\n",
       "      <td>Trade_6fX9pcoH</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.915000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.59</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_1q2mNkKh</td>\n",
       "      <td>Trade_PtGpJWZF</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.392500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>603019</td>\n",
       "      <td>37.48</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_An5xod2g</td>\n",
       "      <td>Trade_D1YwqSTA</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>9.369612</td>\n",
       "      <td>56.217673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>002544</td>\n",
       "      <td>14.62</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_p5FaMcRr</td>\n",
       "      <td>Trade_krqRKVn9</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>600718</td>\n",
       "      <td>14.27</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_P6RCfBtL</td>\n",
       "      <td>Trade_xvaCt3W1</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.397500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>600804</td>\n",
       "      <td>16.51</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_wo9xpg6j</td>\n",
       "      <td>Trade_dCM4bzyl</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>24.765000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2018-01-17 15:00:00</td>\n",
       "      <td>300297</td>\n",
       "      <td>9.56</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_oEip0DhY</td>\n",
       "      <td>Trade_grtUPdf5</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2018-01-19 09:30:00</td>\n",
       "      <td>300051</td>\n",
       "      <td>9.81</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_ljt8XCM9</td>\n",
       "      <td>Trade_KnYN75Za</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.715000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2018-01-19 15:00:00</td>\n",
       "      <td>600804</td>\n",
       "      <td>17.22</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_NiV4FmYK</td>\n",
       "      <td>Trade_L3CkFK0d</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2018-01-22 15:00:00</td>\n",
       "      <td>002095</td>\n",
       "      <td>38.61</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_PeK5RBIJ</td>\n",
       "      <td>Trade_VmH1B8M2</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>9.652500</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2018-04-12 15:00:00</td>\n",
       "      <td>600385</td>\n",
       "      <td>13.10</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_DaA5uWBI</td>\n",
       "      <td>Trade_Jh2Yyaoj</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>002642</td>\n",
       "      <td>14.01</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_UfgBIH1d</td>\n",
       "      <td>Trade_MZ7HlR95</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>600589</td>\n",
       "      <td>5.47</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_PlNE1uoq</td>\n",
       "      <td>Trade_qm8PUXYf</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2018-04-13 09:30:00</td>\n",
       "      <td>600756</td>\n",
       "      <td>17.87</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_Kb1LZd4I</td>\n",
       "      <td>Trade_jF8pc9MT</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>26.805000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>18.80</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_8lEuY9Fx</td>\n",
       "      <td>Trade_TpowHvLg</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>300036</td>\n",
       "      <td>19.69</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_YEGLKufJ</td>\n",
       "      <td>Trade_v9C2ecja</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>29.534717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>600589</td>\n",
       "      <td>5.37</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_3V1sMokx</td>\n",
       "      <td>Trade_5E6KvGgB</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.055000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>600770</td>\n",
       "      <td>7.12</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_YuKcNoaP</td>\n",
       "      <td>Trade_me8O7IqZ</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>18.67</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_XjxTODLM</td>\n",
       "      <td>Trade_t4l318gn</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>300051</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_lEtzAXRO</td>\n",
       "      <td>Trade_LyQufab4</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>600100</td>\n",
       "      <td>10.74</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_tzINe2Bj</td>\n",
       "      <td>Trade_5Vw9AZOo</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>600770</td>\n",
       "      <td>7.51</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_EZkhLb0S</td>\n",
       "      <td>Trade_1YN7C6Eb</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>600850</td>\n",
       "      <td>19.60</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_pSly4RNJ</td>\n",
       "      <td>Trade_tFhPLQZN</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2018-04-20 09:30:00</td>\n",
       "      <td>300051</td>\n",
       "      <td>11.29</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_NDpZB6QW</td>\n",
       "      <td>Trade_K5MdlmCD</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>16.942500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2018-04-20 15:00:00</td>\n",
       "      <td>600589</td>\n",
       "      <td>5.47</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_aLtHfScz</td>\n",
       "      <td>Trade_xSaQMBdK</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>2018-04-20 15:00:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.09</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_aNRh3ew4</td>\n",
       "      <td>Trade_pb1gKVjk</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>2018-04-23 09:30:00</td>\n",
       "      <td>002642</td>\n",
       "      <td>12.42</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_Mc6zbLCw</td>\n",
       "      <td>Trade_QI1xJK5v</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2018-04-23 15:00:00</td>\n",
       "      <td>300366</td>\n",
       "      <td>12.45</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_Tew5CWFN</td>\n",
       "      <td>Trade_8Lb4Hckx</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>2018-04-23 09:30:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.08</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_b4Jwn1i9</td>\n",
       "      <td>Trade_EfSl5VjW</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.612500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2018-04-24 15:00:00</td>\n",
       "      <td>600601</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_OtZfEe9b</td>\n",
       "      <td>Trade_tBv9PxTf</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>2018-04-25 09:30:00</td>\n",
       "      <td>600845</td>\n",
       "      <td>30.00</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_BbZAmTPz</td>\n",
       "      <td>Trade_msU4CXab</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>7.500343</td>\n",
       "      <td>45.002060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>000066</td>\n",
       "      <td>9.28</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_cY47nJdk</td>\n",
       "      <td>Trade_tORLb2G0</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>000611</td>\n",
       "      <td>7.56</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_GH1Rqkt9</td>\n",
       "      <td>Trade_a6GsBcIx</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>000836</td>\n",
       "      <td>4.56</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_GKWiYO3R</td>\n",
       "      <td>Trade_WVfrl0U4</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>002063</td>\n",
       "      <td>11.72</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_t0QnK9dD</td>\n",
       "      <td>Trade_IZB8cFp9</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>002544</td>\n",
       "      <td>18.22</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Order_pxRVawKn</td>\n",
       "      <td>Trade_VFA5vRr9</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>300297</td>\n",
       "      <td>10.64</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_X8e607YD</td>\n",
       "      <td>Trade_JKFDN3vX</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>600385</td>\n",
       "      <td>12.39</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_fNnz7dXv</td>\n",
       "      <td>Trade_N9Ve3h2U</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>18.585000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2018-04-27 09:30:00</td>\n",
       "      <td>002063</td>\n",
       "      <td>11.64</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_fJHRcuAB</td>\n",
       "      <td>Trade_QcYru5DV</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>17.467500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2018-04-27 09:30:00</td>\n",
       "      <td>002368</td>\n",
       "      <td>31.73</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>Order_dy701SjR</td>\n",
       "      <td>Trade_5LgrYZT8</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "      <td>7.932500</td>\n",
       "      <td>47.595000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                datetime    code  price  amount        order_id  \\\n",
       "0    2018-01-02 15:00:00  002195   5.91  1000.0  Order_dRhVu53P   \n",
       "1    2018-01-02 15:00:00  002544  15.85  1000.0  Order_ykw3N6uH   \n",
       "2    2018-01-02 15:00:00  300290   8.71  1000.0  Order_FfO0nlsc   \n",
       "3    2018-01-02 15:00:00  600105   6.58  1000.0  Order_UvYiHIK1   \n",
       "4    2018-01-02 15:00:00  600797  11.87  1000.0  Order_yGimNbhn   \n",
       "5    2018-01-03 15:00:00  000070   9.52  1000.0  Order_l97R514S   \n",
       "6    2018-01-03 15:00:00  002065   8.53  1000.0  Order_5MvgxTLt   \n",
       "7    2018-01-03 15:00:00  300036  15.78  1000.0  Order_cKI5HDl8   \n",
       "8    2018-01-03 15:00:00  600198  11.65  1000.0  Order_bTHhagWP   \n",
       "9    2018-01-03 15:00:00  600718  15.00  1000.0  Order_hHLinCrl   \n",
       "10   2018-01-03 15:00:00  600804  17.93  1000.0  Order_4uv6fj8Q   \n",
       "11   2018-01-03 15:00:00  600850  19.95  1000.0  Order_hCyLzeHQ   \n",
       "12   2018-01-03 15:00:00  603019  42.53  1000.0  Order_U3RVGrve   \n",
       "13   2018-01-05 15:00:00  300051   9.80  1000.0  Order_9qoJpiQZ   \n",
       "14   2018-01-10 09:30:00  600850  19.23 -1000.0  Order_XRiYpusq   \n",
       "15   2018-01-11 15:00:00  002197  12.82  1000.0  Order_pkabzXw9   \n",
       "16   2018-01-11 15:00:00  600601   3.69  1000.0  Order_tqDujrOf   \n",
       "17   2018-01-15 09:30:00  002197  12.06 -1000.0  Order_wKERgClG   \n",
       "18   2018-01-15 09:30:00  300036  14.49 -1000.0  Order_frBKjRAa   \n",
       "19   2018-01-15 09:30:00  300290   8.08 -1000.0  Order_xS0pjWz9   \n",
       "20   2018-01-15 09:30:00  600198  10.61 -1000.0  Order_YvLncXNH   \n",
       "21   2018-01-15 09:30:00  600601   3.59 -1000.0  Order_1q2mNkKh   \n",
       "22   2018-01-15 09:30:00  603019  37.48 -1000.0  Order_An5xod2g   \n",
       "23   2018-01-16 09:30:00  002544  14.62 -1000.0  Order_p5FaMcRr   \n",
       "24   2018-01-16 09:30:00  600718  14.27 -1000.0  Order_P6RCfBtL   \n",
       "25   2018-01-16 09:30:00  600804  16.51 -1000.0  Order_wo9xpg6j   \n",
       "26   2018-01-17 15:00:00  300297   9.56  1000.0  Order_oEip0DhY   \n",
       "27   2018-01-19 09:30:00  300051   9.81 -1000.0  Order_ljt8XCM9   \n",
       "28   2018-01-19 15:00:00  600804  17.22  1000.0  Order_NiV4FmYK   \n",
       "29   2018-01-22 15:00:00  002095  38.61  1000.0  Order_PeK5RBIJ   \n",
       "..                   ...     ...    ...     ...             ...   \n",
       "105  2018-04-12 15:00:00  600385  13.10  1000.0  Order_DaA5uWBI   \n",
       "106  2018-04-13 15:00:00  002642  14.01  1000.0  Order_UfgBIH1d   \n",
       "107  2018-04-13 15:00:00  600589   5.47  1000.0  Order_PlNE1uoq   \n",
       "108  2018-04-13 09:30:00  600756  17.87 -1000.0  Order_Kb1LZd4I   \n",
       "109  2018-04-13 15:00:00  600850  18.80  1000.0  Order_8lEuY9Fx   \n",
       "110  2018-04-17 09:30:00  300036  19.69 -1000.0  Order_YEGLKufJ   \n",
       "111  2018-04-17 09:30:00  600589   5.37 -1000.0  Order_3V1sMokx   \n",
       "112  2018-04-17 09:30:00  600770   7.12 -1000.0  Order_YuKcNoaP   \n",
       "113  2018-04-17 09:30:00  600850  18.67 -1000.0  Order_XjxTODLM   \n",
       "114  2018-04-18 15:00:00  300051  12.00  1000.0  Order_lEtzAXRO   \n",
       "115  2018-04-18 15:00:00  600100  10.74  1000.0  Order_tzINe2Bj   \n",
       "116  2018-04-18 15:00:00  600770   7.51  1000.0  Order_EZkhLb0S   \n",
       "117  2018-04-18 15:00:00  600850  19.60  1000.0  Order_pSly4RNJ   \n",
       "118  2018-04-20 09:30:00  300051  11.29 -1000.0  Order_NDpZB6QW   \n",
       "119  2018-04-20 15:00:00  600589   5.47  1000.0  Order_aLtHfScz   \n",
       "120  2018-04-20 15:00:00  600601   3.09  1000.0  Order_aNRh3ew4   \n",
       "121  2018-04-23 09:30:00  002642  12.42 -1000.0  Order_Mc6zbLCw   \n",
       "122  2018-04-23 15:00:00  300366  12.45  1000.0  Order_Tew5CWFN   \n",
       "123  2018-04-23 09:30:00  600601   3.08 -1000.0  Order_b4Jwn1i9   \n",
       "124  2018-04-24 15:00:00  600601   3.08  1000.0  Order_OtZfEe9b   \n",
       "125  2018-04-25 09:30:00  600845  30.00 -1000.0  Order_BbZAmTPz   \n",
       "126  2018-04-26 09:30:00  000066   9.28 -1000.0  Order_cY47nJdk   \n",
       "127  2018-04-26 15:00:00  000611   7.56  1000.0  Order_GH1Rqkt9   \n",
       "128  2018-04-26 15:00:00  000836   4.56  1000.0  Order_GKWiYO3R   \n",
       "129  2018-04-26 15:00:00  002063  11.72  1000.0  Order_t0QnK9dD   \n",
       "130  2018-04-26 15:00:00  002544  18.22  1000.0  Order_pxRVawKn   \n",
       "131  2018-04-26 09:30:00  300297  10.64 -1000.0  Order_X8e607YD   \n",
       "132  2018-04-26 09:30:00  600385  12.39 -1000.0  Order_fNnz7dXv   \n",
       "133  2018-04-27 09:30:00  002063  11.64 -1000.0  Order_fJHRcuAB   \n",
       "134  2018-04-27 09:30:00  002368  31.73 -1000.0  Order_dy701SjR   \n",
       "\n",
       "           trade_id account_cookie  commission        tax  \n",
       "0    Trade_Xn1l6LPZ   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "1    Trade_cOpw7vmN   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "2    Trade_sZ61KjYP   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "3    Trade_lq6b7k9Z   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "4    Trade_fPUCQ01t   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "5    Trade_OhtnrxDC   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "6    Trade_rm7aYq5w   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "7    Trade_omAkLOGx   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "8    Trade_AHe9zjXL   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "9    Trade_gGVZT4uc   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "10   Trade_FvJX1o0r   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "11   Trade_Dlg8ahqj   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "12   Trade_okmF7CPM   JCSC_EXAMPLE   10.632202   0.000000  \n",
       "13   Trade_MZ4NOVE5   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "14   Trade_xY9P4eGr   JCSC_EXAMPLE    5.000000  28.852500  \n",
       "15   Trade_Qk7bjUyo   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "16   Trade_CizLsXG8   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "17   Trade_E3UtcdWy   JCSC_EXAMPLE    5.000000  18.090000  \n",
       "18   Trade_lahUpuOV   JCSC_EXAMPLE    5.000000  21.732953  \n",
       "19   Trade_TSf9Mva1   JCSC_EXAMPLE    5.000000  12.120000  \n",
       "20   Trade_6fX9pcoH   JCSC_EXAMPLE    5.000000  15.915000  \n",
       "21   Trade_PtGpJWZF   JCSC_EXAMPLE    5.000000   5.392500  \n",
       "22   Trade_D1YwqSTA   JCSC_EXAMPLE    9.369612  56.217673  \n",
       "23   Trade_krqRKVn9   JCSC_EXAMPLE    5.000000  21.930000  \n",
       "24   Trade_xvaCt3W1   JCSC_EXAMPLE    5.000000  21.397500  \n",
       "25   Trade_dCM4bzyl   JCSC_EXAMPLE    5.000000  24.765000  \n",
       "26   Trade_grtUPdf5   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "27   Trade_KnYN75Za   JCSC_EXAMPLE    5.000000  14.715000  \n",
       "28   Trade_L3CkFK0d   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "29   Trade_VmH1B8M2   JCSC_EXAMPLE    9.652500   0.000000  \n",
       "..              ...            ...         ...        ...  \n",
       "105  Trade_Jh2Yyaoj   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "106  Trade_MZ7HlR95   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "107  Trade_qm8PUXYf   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "108  Trade_jF8pc9MT   JCSC_EXAMPLE    5.000000  26.805000  \n",
       "109  Trade_TpowHvLg   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "110  Trade_v9C2ecja   JCSC_EXAMPLE    5.000000  29.534717  \n",
       "111  Trade_5E6KvGgB   JCSC_EXAMPLE    5.000000   8.055000  \n",
       "112  Trade_me8O7IqZ   JCSC_EXAMPLE    5.000000  10.687500  \n",
       "113  Trade_t4l318gn   JCSC_EXAMPLE    5.000000  28.005000  \n",
       "114  Trade_LyQufab4   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "115  Trade_5Vw9AZOo   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "116  Trade_1YN7C6Eb   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "117  Trade_tFhPLQZN   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "118  Trade_K5MdlmCD   JCSC_EXAMPLE    5.000000  16.942500  \n",
       "119  Trade_xSaQMBdK   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "120  Trade_pb1gKVjk   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "121  Trade_QI1xJK5v   JCSC_EXAMPLE    5.000000  18.630000  \n",
       "122  Trade_8Lb4Hckx   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "123  Trade_EfSl5VjW   JCSC_EXAMPLE    5.000000   4.612500  \n",
       "124  Trade_tBv9PxTf   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "125  Trade_msU4CXab   JCSC_EXAMPLE    7.500343  45.002060  \n",
       "126  Trade_tORLb2G0   JCSC_EXAMPLE    5.000000  13.920000  \n",
       "127  Trade_a6GsBcIx   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "128  Trade_WVfrl0U4   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "129  Trade_IZB8cFp9   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "130  Trade_VFA5vRr9   JCSC_EXAMPLE    5.000000   0.000000  \n",
       "131  Trade_JKFDN3vX   JCSC_EXAMPLE    5.000000  15.960000  \n",
       "132  Trade_N9Ve3h2U   JCSC_EXAMPLE    5.000000  18.585000  \n",
       "133  Trade_QcYru5DV   JCSC_EXAMPLE    5.000000  17.467500  \n",
       "134  Trade_5LgrYZT8   JCSC_EXAMPLE    7.932500  47.595000  \n",
       "\n",
       "[135 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.history_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>cash</th>\n",
       "      <th>datetime</th>\n",
       "      <th>date</th>\n",
       "      <th>account_cookie</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">2018-01-02 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>194085</td>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "      <td>178230</td>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
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       "      <td>2018-01-02 15:00:00</td>\n",
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       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>162930</td>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
       "      <td>2018-01-02</td>\n",
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       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>151055</td>\n",
       "      <td>2018-01-02 15:00:00</td>\n",
       "      <td>2018-01-02</td>\n",
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       "      <th rowspan=\"8\" valign=\"top\">2018-01-03 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>141530</td>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
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       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>132995</td>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>117210</td>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>105555</td>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>90550</td>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>72615</td>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>52660</td>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>10119.4</td>\n",
       "      <td>2018-01-03 15:00:00</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>314.368</td>\n",
       "      <td>2018-01-05 15:00:00</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>19510.5</td>\n",
       "      <td>2018-01-10 09:30:00</td>\n",
       "      <td>2018-01-10</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-01-11 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>6685.52</td>\n",
       "      <td>2018-01-11 15:00:00</td>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>2990.52</td>\n",
       "      <td>2018-01-11 15:00:00</td>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2018-01-15 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>15027.4</td>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>29490.7</td>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>37553.6</td>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>48142.7</td>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>51722.3</td>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>89136.7</td>\n",
       "      <td>2018-01-15 09:30:00</td>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2018-01-16 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>103730</td>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>2018-01-16</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>117973</td>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>2018-01-16</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>134454</td>\n",
       "      <td>2018-01-16 09:30:00</td>\n",
       "      <td>2018-01-16</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>124889</td>\n",
       "      <td>2018-01-17 15:00:00</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>134679</td>\n",
       "      <td>2018-01-19 09:30:00</td>\n",
       "      <td>2018-01-19</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>117454</td>\n",
       "      <td>2018-01-19 15:00:00</td>\n",
       "      <td>2018-01-19</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>78834.2</td>\n",
       "      <td>2018-01-22 15:00:00</td>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-12 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>21169.8</td>\n",
       "      <td>2018-04-12 15:00:00</td>\n",
       "      <td>2018-04-12</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-04-13 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>7154.84</td>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1679.84</td>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-13 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>19518</td>\n",
       "      <td>2018-04-13 09:30:00</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-13 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>713.034</td>\n",
       "      <td>2018-04-13 15:00:00</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2018-04-17 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>20368.5</td>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>25725.4</td>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>32829.8</td>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>51466.8</td>\n",
       "      <td>2018-04-17 09:30:00</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2018-04-18 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>39461.8</td>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>28716.8</td>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>21201.8</td>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1596.75</td>\n",
       "      <td>2018-04-18 15:00:00</td>\n",
       "      <td>2018-04-18</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-20 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>12864.8</td>\n",
       "      <td>2018-04-20 09:30:00</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-04-20 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>7389.81</td>\n",
       "      <td>2018-04-20 15:00:00</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>4294.81</td>\n",
       "      <td>2018-04-20 15:00:00</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>16691.2</td>\n",
       "      <td>2018-04-23 09:30:00</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>4236.18</td>\n",
       "      <td>2018-04-23 15:00:00</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-23 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>7306.57</td>\n",
       "      <td>2018-04-23 09:30:00</td>\n",
       "      <td>2018-04-23</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-24 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>4221.57</td>\n",
       "      <td>2018-04-24 15:00:00</td>\n",
       "      <td>2018-04-24</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-25 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>34169.1</td>\n",
       "      <td>2018-04-25 09:30:00</td>\n",
       "      <td>2018-04-25</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-26 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>43430.1</td>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2018-04-26 15:00:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>35865.1</td>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>31300.1</td>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>19575.1</td>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>1350.14</td>\n",
       "      <td>2018-04-26 15:00:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-04-26 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>11969.2</td>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>24335.6</td>\n",
       "      <td>2018-04-26 09:30:00</td>\n",
       "      <td>2018-04-26</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-04-27 09:30:00</th>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>35953.1</td>\n",
       "      <td>2018-04-27 09:30:00</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JCSC_EXAMPLE</th>\n",
       "      <td>67627.6</td>\n",
       "      <td>2018-04-27 09:30:00</td>\n",
       "      <td>2018-04-27</td>\n",
       "      <td>JCSC_EXAMPLE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       cash             datetime       date  \\\n",
       "datetime            account_cookie                                            \n",
       "2018-01-02 15:00:00 JCSC_EXAMPLE     194085  2018-01-02 15:00:00 2018-01-02   \n",
       "                    JCSC_EXAMPLE     178230  2018-01-02 15:00:00 2018-01-02   \n",
       "                    JCSC_EXAMPLE     169515  2018-01-02 15:00:00 2018-01-02   \n",
       "                    JCSC_EXAMPLE     162930  2018-01-02 15:00:00 2018-01-02   \n",
       "                    JCSC_EXAMPLE     151055  2018-01-02 15:00:00 2018-01-02   \n",
       "2018-01-03 15:00:00 JCSC_EXAMPLE     141530  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE     132995  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE     117210  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE     105555  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE      90550  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE      72615  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE      52660  2018-01-03 15:00:00 2018-01-03   \n",
       "                    JCSC_EXAMPLE    10119.4  2018-01-03 15:00:00 2018-01-03   \n",
       "2018-01-05 15:00:00 JCSC_EXAMPLE    314.368  2018-01-05 15:00:00 2018-01-05   \n",
       "2018-01-10 09:30:00 JCSC_EXAMPLE    19510.5  2018-01-10 09:30:00 2018-01-10   \n",
       "2018-01-11 15:00:00 JCSC_EXAMPLE    6685.52  2018-01-11 15:00:00 2018-01-11   \n",
       "                    JCSC_EXAMPLE    2990.52  2018-01-11 15:00:00 2018-01-11   \n",
       "2018-01-15 09:30:00 JCSC_EXAMPLE    15027.4  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    29490.7  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    37553.6  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    48142.7  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    51722.3  2018-01-15 09:30:00 2018-01-15   \n",
       "                    JCSC_EXAMPLE    89136.7  2018-01-15 09:30:00 2018-01-15   \n",
       "2018-01-16 09:30:00 JCSC_EXAMPLE     103730  2018-01-16 09:30:00 2018-01-16   \n",
       "                    JCSC_EXAMPLE     117973  2018-01-16 09:30:00 2018-01-16   \n",
       "                    JCSC_EXAMPLE     134454  2018-01-16 09:30:00 2018-01-16   \n",
       "2018-01-17 15:00:00 JCSC_EXAMPLE     124889  2018-01-17 15:00:00 2018-01-17   \n",
       "2018-01-19 09:30:00 JCSC_EXAMPLE     134679  2018-01-19 09:30:00 2018-01-19   \n",
       "2018-01-19 15:00:00 JCSC_EXAMPLE     117454  2018-01-19 15:00:00 2018-01-19   \n",
       "2018-01-22 15:00:00 JCSC_EXAMPLE    78834.2  2018-01-22 15:00:00 2018-01-22   \n",
       "...                                     ...                  ...        ...   \n",
       "2018-04-12 15:00:00 JCSC_EXAMPLE    21169.8  2018-04-12 15:00:00 2018-04-12   \n",
       "2018-04-13 15:00:00 JCSC_EXAMPLE    7154.84  2018-04-13 15:00:00 2018-04-13   \n",
       "                    JCSC_EXAMPLE    1679.84  2018-04-13 15:00:00 2018-04-13   \n",
       "2018-04-13 09:30:00 JCSC_EXAMPLE      19518  2018-04-13 09:30:00 2018-04-13   \n",
       "2018-04-13 15:00:00 JCSC_EXAMPLE    713.034  2018-04-13 15:00:00 2018-04-13   \n",
       "2018-04-17 09:30:00 JCSC_EXAMPLE    20368.5  2018-04-17 09:30:00 2018-04-17   \n",
       "                    JCSC_EXAMPLE    25725.4  2018-04-17 09:30:00 2018-04-17   \n",
       "                    JCSC_EXAMPLE    32829.8  2018-04-17 09:30:00 2018-04-17   \n",
       "                    JCSC_EXAMPLE    51466.8  2018-04-17 09:30:00 2018-04-17   \n",
       "2018-04-18 15:00:00 JCSC_EXAMPLE    39461.8  2018-04-18 15:00:00 2018-04-18   \n",
       "                    JCSC_EXAMPLE    28716.8  2018-04-18 15:00:00 2018-04-18   \n",
       "                    JCSC_EXAMPLE    21201.8  2018-04-18 15:00:00 2018-04-18   \n",
       "                    JCSC_EXAMPLE    1596.75  2018-04-18 15:00:00 2018-04-18   \n",
       "2018-04-20 09:30:00 JCSC_EXAMPLE    12864.8  2018-04-20 09:30:00 2018-04-20   \n",
       "2018-04-20 15:00:00 JCSC_EXAMPLE    7389.81  2018-04-20 15:00:00 2018-04-20   \n",
       "                    JCSC_EXAMPLE    4294.81  2018-04-20 15:00:00 2018-04-20   \n",
       "2018-04-23 09:30:00 JCSC_EXAMPLE    16691.2  2018-04-23 09:30:00 2018-04-23   \n",
       "2018-04-23 15:00:00 JCSC_EXAMPLE    4236.18  2018-04-23 15:00:00 2018-04-23   \n",
       "2018-04-23 09:30:00 JCSC_EXAMPLE    7306.57  2018-04-23 09:30:00 2018-04-23   \n",
       "2018-04-24 15:00:00 JCSC_EXAMPLE    4221.57  2018-04-24 15:00:00 2018-04-24   \n",
       "2018-04-25 09:30:00 JCSC_EXAMPLE    34169.1  2018-04-25 09:30:00 2018-04-25   \n",
       "2018-04-26 09:30:00 JCSC_EXAMPLE    43430.1  2018-04-26 09:30:00 2018-04-26   \n",
       "2018-04-26 15:00:00 JCSC_EXAMPLE    35865.1  2018-04-26 15:00:00 2018-04-26   \n",
       "                    JCSC_EXAMPLE    31300.1  2018-04-26 15:00:00 2018-04-26   \n",
       "                    JCSC_EXAMPLE    19575.1  2018-04-26 15:00:00 2018-04-26   \n",
       "                    JCSC_EXAMPLE    1350.14  2018-04-26 15:00:00 2018-04-26   \n",
       "2018-04-26 09:30:00 JCSC_EXAMPLE    11969.2  2018-04-26 09:30:00 2018-04-26   \n",
       "                    JCSC_EXAMPLE    24335.6  2018-04-26 09:30:00 2018-04-26   \n",
       "2018-04-27 09:30:00 JCSC_EXAMPLE    35953.1  2018-04-27 09:30:00 2018-04-27   \n",
       "                    JCSC_EXAMPLE    67627.6  2018-04-27 09:30:00 2018-04-27   \n",
       "\n",
       "                                   account_cookie  \n",
       "datetime            account_cookie                 \n",
       "2018-01-02 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-03 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-05 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-10 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-11 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-15 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-16 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-17 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-19 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-19 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-01-22 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "...                                           ...  \n",
       "2018-04-12 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-13 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-13 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-13 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-17 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-18 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-20 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-20 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-23 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-23 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-23 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-24 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-25 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-26 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-26 15:00:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-26 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "2018-04-27 09:30:00 JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "                    JCSC_EXAMPLE     JCSC_EXAMPLE  \n",
       "\n",
       "[135 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Account.cash_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2018-01-02    199975.000000\n",
       "2018-01-03    200679.367798\n",
       "2018-01-05    191164.367798\n",
       "2018-01-10    187760.515298\n",
       "2018-01-11    202340.515298\n",
       "2018-01-15    189316.677560\n",
       "2018-01-16    189833.585060\n",
       "2018-01-17    187938.585060\n",
       "2018-01-19    188043.870060\n",
       "2018-01-22    189519.217560\n",
       "2018-01-23    194150.842560\n",
       "2018-01-24    200345.842560\n",
       "2018-01-26    198190.842560\n",
       "2018-01-29    193573.772560\n",
       "2018-01-30    194848.785060\n",
       "2018-01-31    188683.790060\n",
       "2018-02-01    186996.314850\n",
       "2018-02-06    184640.553600\n",
       "2018-02-12    187834.902675\n",
       "2018-02-13    190619.555175\n",
       "2018-02-14    189239.555175\n",
       "2018-02-22    194563.697675\n",
       "2018-02-23    192878.697675\n",
       "2018-03-14    221235.480877\n",
       "2018-03-15    212227.860551\n",
       "2018-03-16    210632.860551\n",
       "2018-03-19    212609.521801\n",
       "2018-03-20    214408.851801\n",
       "2018-03-21    212304.776801\n",
       "2018-03-22    211804.009301\n",
       "2018-03-23    205666.791257\n",
       "2018-03-26    207276.195925\n",
       "2018-03-27    213178.835925\n",
       "2018-03-28    210206.280925\n",
       "2018-03-29    212861.280925\n",
       "2018-04-02    224145.294675\n",
       "2018-04-04    215305.629675\n",
       "2018-04-09    217638.774675\n",
       "2018-04-10    213378.509554\n",
       "2018-04-11    213446.097281\n",
       "2018-04-12    212219.838531\n",
       "2018-04-13    215233.033531\n",
       "2018-04-17    203246.751314\n",
       "2018-04-18    211716.751314\n",
       "2018-04-20    208454.808814\n",
       "2018-04-23    201826.566314\n",
       "2018-04-24    208451.566314\n",
       "2018-04-25    205199.063910\n",
       "2018-04-26    198195.598910\n",
       "2018-04-27    204497.603910\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d32c9fda20>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.assets.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d32e5dc550>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.benchmark_assets.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.plot_assets_curve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.plot_dailyhold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1296 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Risk.plot_signal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total_buyandsell': 6599.99,\n",
       " 'total_tax': -1372.19,\n",
       " 'total_commission': -730.2,\n",
       " 'total_profit': 4497.6}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Risk.profit_construct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Performance=QA.QA_Performance(Account)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JYEiSU1dXF1u2bGHp0qU4nc6LltP1vj8qoGlan2UVFRVUVFQAsGnTpoiENlg2my2qz0dbTmKOwJheL0z9ITabjUAgYN56SszhEdPrJbBhO+2/+gWhxgYsbg+p9z6ILS8/qtiJKubJKRAIsGXLFubNm8cNN9wAgMvlorGxEbfbTWNjIxkZGUBvTyn8QcKGhoZ+e02lpaWUlpaqvz/Nw4feKB9ejLacxJSYElNiDnlMmwPuf0iVawIY5HUwPz+xkllM55x0XWfHjh0UFBSwZMkStby4uJjKykoAKisrmT17tlq+Z88edF3nyJEjOJ1OGdITQggR257TRx99xJ49exg7dizf/va3Abj33nu54447KCsrY/fu3Xi9XtauXQvAjBkz2LdvHytXrsThcLB8+fJYVkcIIUxtJD5cG62YJqcpU6bwP//zP/3+27p16/os0zSNZcuWxbIKQgiREORtIwOTN0QIIUQ8yNtGBiTJSQgh4mCkPlwbLXnxqxjWZExfmJWWmUXfh2mG/8O10ZLkJEwj1olExvSFqY3Qh2ujJclJmMKQJJIR+sJMkRjkbSMDk+QkzGEIEomM6Quzk182vji5IUKYwlAkkpH6wkwhhgNJTsIUhiSRyBvEhUhYMqwnzGEIJodlTF+IxCXJSZjCUCUSGdMXIjFJchKmIYlECGGQOSchhBCmI8lJCCGE6UhyEkIIYToy5ySEGHbknYqJT5KTEGJYkXcqDg8yrCeEGF7kd5KGBUlOQohhRd6pODxIchJCDCvyTsXhIeZzTj/72c/Yt28fLpeLLVu2ANDW1kZZWRl1dXVkZ2ezZs0a0tLS0HWdXbt2sX//fpKSkli+fDkTJkyIdZWEECOJ/E7SsBDzntPChQv57ne/G7GsvLycoqIitm3bRlFREeXl5QDs37+fmpoatm3bxoMPPsjOnTtjXR0hxAhjyc5DW7MB7YYF2K+ZiXbDAjS5GSLhxDw5TZs2jbS0tIhlVVVVLFiwAIAFCxZQVVUFQHV1NfPnz0fTNCZPnkx7ezuNjY2xrpIQYoSxZOdhWfYwWU9ux7LsYUlMCeiK3Ere3NyM2+0GwO1209LSAoDP58Pr9apyHo8Hn8+nyhoqKiqoqKgAYNOmTRGfGSybzRbV56MtJzElZizKBWrO0v6rX9DYWI/d7SX13gex5eWbrp4SM3FiJrq4Puek63qfZZqm9VlWWlpKaWmp+vvTvBTUG+VLRaMtJzEl5qctF/5cjqHrw78OOBQ1XNZdYg5dzAvl51+8sWNGVyQ5uVwuGhsbcbvdNDY2kpGRAfT2lMI3dENDQ59ekxDD3hD8RL2IH3k7RWxckVvJi4uLqaysBKCyspLZs2er5Xv27EHXdY4cOYLT6ZTkJEYceS5n+DB6wfo7lfS8vw/9nUr0snW9CUsMSsx7Tlu3buWDDz6gtbWVb33rW9x1113ccccdlJWVsXv3brxeL2vXrgVgxowZ7Nu3j5UrV+JwOFi+fHmsqyNEVOLZ2tUys+g7wC3P5VwoIXok0guOmZgnp9WrV/e7fN26dX2WaZrGsmXLYl0FIQZlsO9ii/lFUp7LuaREeV+e9IJjR94QIcQg3sU2FMM28lxOFBLkfXnydorYkbeSixFvUK3dIRq2kZ+oH1jC9EgG2QtOiKHKOJHkJEa8wcz5JMxFcphJlHk5S3YeoTUb4DcvYmtvJTBAwkmUocp4keQkxCBau4lykRx2EmheLupesNw8MSBJTmLEG0xrN5EuksPJoPZRgpBe+MAkOQkxCMPxIpkohtu8nPTCBybJSYx4gx37H24XSREn0gsfkCQnoYzYO4dk7F/EgfTCBybJSQAj+84hGfsX8SK98IuTh3BFrwR5yHEoyIOTQpiP9JxMJl5DayO69yBj/1EbsUO/4oqT5GQi8RxaG8l3DsnYf3RG8tCvuPJkWM9M4jm09qX7ensL4UZQ70F+1jsKI3joV1x50nMykXgOrUnvQVzKiB76FVecJCcTiffQmtw5JAYyVMenzGOJ/khyMhOZmBdmNgTHp8xjiYuR5GQiw3FoTVrFw8eQHJ/yALS4CElOJjOchtakVTz8xPr4lHkscTGmuFvvwIEDrFq1ihUrVlBeXh7v6ohYkbu7xKUkpwxuuRgx4t5zCoVCPPvss3z/+9/H4/Hw2GOPUVxczOjRo+NdtZgZqUNb0ioWQlyuuCenY8eOkZeXR25uLgA33ngjVVVVwyY5jeShrXjffSgSQFfn4JbH0UhtZMZL3JOTz+fD4/Govz0eD0ePHo1jjWIsgSZ8Y37yyd2H4hIS5fb0eDcyR2JijHty0vW+h6amaRF/V1RUUFFRAcCmTZvwer2X/X02my2qz0db7lJlfe2tfz+Ywz/T3krWAPGvdD0DNWdp+ukTBM+fUfW1njxG5uM/xZaXf3nf7/US2LCd9l/9glBjAxa3h9R7Hxww3iVjXkY5iWnemIGlK2g6eYzg+TNqmTW3gMylK7Bd5HOXjHkZx/KlYja/sJ2ufhqZSb9/Gdeaxy8rZrRlL/fcTHRxT04ej4eGhgb1d0NDA263O6JMaWkppaWl6u9Pc5eQN8q7jKItd6myodT0fpcHUtMHjH/F6/n80+hhFwiA4Pkz+J5/GssAPbxLfr/NAfc/pMo1AVyivld63SVmHGPaHIRWrUcLuz099KX7aLI5LnqcXCrm5RzLl4oZPH+u3+Vd58/Rc5n1jLbs5Z6bF8rPT6xEFvfkNHHiRM6dO0dtbS1ZWVns3buXlStXxrtasZMgQ1ty84KIl0S4PT2e86cj9dyMe3KyWq088MADbNy4kVAoxM0338yYMWPiXa2YSZQHawd78o3EMXCRIIbi9vQ4NjJH6o1FcU9OADNnzmTmzJnxrgYwNBfdhHiw9kv3wZFD0BhWP7e335Mv3pPDQgyou2twy6MQ10Zmgoy+xJopkpNZjPiL7gU3ovT525BAdyCKSxt2veAzpwa3PErxamQmyuhLrElyCjdML7pRXXx+8yL46iKX+er6XfeROgaeaKLZ7yO+QZYgEmL0JcZGTHKK5kQdjhfdaC8+g1n3kToGnkiiTjrDsUE24TPw3l/6Xy4ShinerTfUjBNVf6eSnvf3ob9TiV62rjdhhbnYxTWhL7pRvt9uMOuuFxX3W/Ziy0UcRLnfh2ODTLt7GWRlRy7Myu5dLhLGyOg5Rds6TKCJx2jnCaK++Axm3V95of9KvfIC3LAg2lUQQyja/T4c79K0ZOcRemTjiJujGW5GRHKK9kQd7MRjvE7UwcwTRHvxGdS6d7T3X7GLLRdXXNRJZxCNkkSanxqJczTDzYhIToNpHUZ7UMf1RB3MPMEgLj5Rn9DOVOjsJxE5UwexEmJIRbnfB9UoGY7zU8K0RkRyGpLhujieqIOZJxiS21CXroKydRAKhn2RtXe5MIXB7PdoGyXDcX4q3hJhmDReRkRyGsyJGvO5nCEw2HmCWA9xWKcUEVyzAZ7/KXR2QIoTlq7COqWoT1k5+eIn1vt9MMddouz3eNYzkYZJ42FEJCeI7kQdirmcIWGCGzesU4pg086BX1gpJ9/wEuVxlyj7Pe71lGHSAY2Y5BSVIZrLibWhemJ8MK3IqB/sHcEnX6L0HqIV9XGXKPs9zvWUYdKBSXIKE/e5nEGI9ZDNYFqRQ/Fg73ATqqtB3/xdaKz/+zY6cojQt3+Q8AnqUsddouz3eNdTHmYf2Ih4CDdag30I15Kdh2XZw2Q9uR3LsocT+qIT7UObgyk7LB9qjpL+0s7Il+gCNNb3Lh/mEmW/x72eX7qvd7QlnEmfq4wHSU5h9Jtu6b3rLJzF2rt8mBtMK1Kv7f+H1/osH8kn3/GPBrd8OEmU/R7nelqy89DWbEC7YQH2a2ai3bAAzWTzcvEkw3phtP/3Onr47dEAoSDa/3sd+rkTLXj4IDz/U85f4o61RDCoIYaWpv6DXLA83kOfIj4SZb+boZ7ysPDFSXIKM5jeQ/DwwchnfTraoGwdwTUbEjNBDeYGjww3NNT2Xe5y91k0Yk++Ef7y0UTZ74lSz5FIhvXCDeYXNJ//aeRDqND79/M/jX29roBBDTFkuPoPkn6R5SOQvHxUiE9Hek6Xaxi+X05akbEjLx8V4tOR5BQuyrkUYGS/X66rc3DLRyhJ9kJcvpgN6/35z39m7dq13H333Xz88ccR//bKK6+wYsUKVq1axYEDB9TyAwcOsGrVKlasWEF5eXmsqnL5BpOclq7q986+kfB+ubjfgiuEGPZilpzGjBnDI488wtSpUyOWnz59mr179/KTn/yE733vezz77LOEQiFCoRDPPvss3/3udykrK+Ott97i9OnTsarO5cnoO6EP9DvRb51SBGs2gCcHnGm9/03UmyEGK1FuFRZCJKyYDeuNHj263+VVVVXceOON2O12cnJyyMvL49ixYwDk5eWRm5sLwI033khVVdVF41wJWk4e+om+z6FoF5kniOb9csORGW7BFUIMb0M+5+Tz+Zg0aZL6OysrC5+v99Zsj8ejlns8Ho4ePTrU1RmYCV6omihkPkUIMZQGlZyefPJJmpr6zr/cc889zJ49u9/P6Hp/j3b2v1zTtH7LVlRUUFFRAcCmTZvwer3RVrkPm8128c97vQQ2bKf9V78g1NiAxe0h9d4HseXlX37Myyg3nGIGas7S/qtf0NhYj93tle0pMRM25mCP5Xive6IbVHL693//90F/gcfjoaGhQf3t8/nIyuqdOA9f3tDQgNvd/5xPaWkppaWl6u9P01K/5BCczQH3P6TKNfV+4aeLOchywyVm+AtiDV0f/vWSr2gZDusuMYdXzMs5luO97hfKzx+4UWg2Q/4QbnFxMXv37qWnp4fa2lrOnTvH1VdfzcSJEzl37hy1tbUEAgH27t1LcXHxUFfnkkJ1NYR2bsH37w8R2rml92cPxOUZzMtkhTAzOZavuJjNOf3lL3/hueeeo6WlhU2bNjFu3Di+973vMWbMGObMmcPatWuxWCz80z/9ExZLb0584IEH2LhxI6FQiJtvvpkxY8bEqjqXJe4/PjbMxPsnCYSIFTmWr7yYJafrr7+e66+/vt9/u/POO7nzzjv7LJ85cyYzZ86MVRU+vUT5kbQEIb9XI4YLOZavPHm3XhhpHcWYPA8lhgs5lq84eX1RGGkdxZY8DyWGCzmWrzxJTuHkOaeYk+ehxHAhx/KVJckpjLSOhBDCHCQ5XUBaR0IIEX9yQ4QQQgjTkeQkhBDCdCQ5CSGEMB1JTkIIIUxHkpMQQgjT0fSL/aaFEEIIEScjruf06KOPxrScxJSYElNimjFmohtxyUkIIYT5SXISQghhOtbHH3/88XhX4kqbMGFCTMtJTIkpMSWmGWMmMrkhQgghhOnIsJ4QQgjTGfDFr/X19TzzzDM0NTWhaRqlpaXcfvvtALS1tVFWVkZdXR3Z2dl8/etfZ9euXdTV1dHU1EQgEOCrX/0qX/ziF1XZkydP0t3dDYDVaiUjI4NgMEhTUxP33HMPixYtoqysjL/97W+0tbWh6zoWiwWr1Yqu6/T09JCSkkIwGOTnP/85ZWVlHD16lK6uLgDsdjtZWVm0tLTQ2dkJgMPh4DOf+YyKGQwGAdA0DZvNRk9PD5qmoes6zz33HGVlZRw+fJieHvVD7dhsNjRNi1jm9Xrp6emhtbWVUCiklhuxDG63G13XaW9vj/i8xWLBYrEQCATUspSUFOx2Oy0tLRH74cKYAGlpaXR3d0fE1DQNi8Wi1tFYlpycrLbHQDGdTid+vz+iTklJSSQlJfWpU0pKSlQxMzIy6Ozs7FNPTdMithtAcnKy2pcGi8XSp1xmZiZtbW0R9exv3Y116ujouKx6WiwWNE3rE7O/de+vnl6vl+bm5j4xgT5l+6tnfzGN4zt83W02G3a7vU+dUlNTaW9vjypmc3NzxHr2d8wPJqbX61XXAUN/x+ynXXer1YrFYomqnv3pbx+lp6fT1dXV55j9/Oc/z+9+9zt17Giaxrx583jnnXfw+/1qeWlpKU1NTVRXV0fETE5OpqGhQa2XxWJhw4YN7Nq1i+PHj6vrXUFBAbW1tSqmpmnccMMNFBYW8txzz6nvSUtLIz09nZ6eHnw+H6FQiJSUFL797W/z3HPPcfbsWUKhEOnp6cwkg7lcAAAWBElEQVSZM4ePPvqI8+fP09XVhdVqZe7cuXzzm9/kySef5OjRo1gsFj7/+c9TUlLCf/7nf9Lc3ExbWxvp6en88z//M2+++SYffvgh7e3tuFwubrnlFu644w5eeeUVdu/ejcVi4Rvf+AaFhYVs376d48ePk56ezurVq8nJyQHoU/a666676L4ZsOdktVq5//77KSsrY+PGjbz22mucPn0agPLycoqKiti2bRtFRUW8/vrr3H///WzcuJHHHnuMlJQUmpqaVNmJEyeSnJzMrbfeSklJCddeey2lpaXous6CBQtUucmTJ2Oz2UhKSmLevHmkp6czZcoUbrvtNpKTk5k7d64qO2bMGPx+P7fddhs33ngjFouFefPm4XA4uPbaa7FarQSDQRXTYrGQl5eH2+1m7NixeL1e9T1GzNzcXHp6epg6daraoLfffrtKHFarFavVitPpRNd1dF0nJSWFzMxMkpOTKSkpweFw4HA4AGhvb8fj8agkOHbsWDRNw263A70JtbS0FAC/368uME6nk+TkZHUSWK1WoDfZapoWkexGjRoVcRLZ7XZSUlLUMiOm3W7H6XSq/zfq8MgjjwDQ0dGhTvyrr74agO7ubtra2tRns7KyImKmpKSgaRrQm4iN/3/ssccAaGlpUfVMS0sDUNvMbrejaRoLFy4EiGhkGHGSk5PVhTInJwdN0yIuULm5uWq9je1usVhYtGiRWicjprE9LRaLWveHH364Tz2N7RkKhVTZjIwMtU3Dt6eRaI16Wq1WfvSjHwG9jTtjv0+fPr1PzJSUFJWsjHrabDZVz/CLs3ES+3w+AoEAmqZRWFgIQCAQoLOzE4fDgcvlUseKcXE2jlvj/w3G530+n0pMEydOVDGN/WuxWMjOzo6IabVaSUpKUrGMfWRsf6OeNpuNqVOnqs86HA7sdntETGPdjX8Ljwmo72lvbycQCGCxWJg5cyYAwWAQXddVksrLy4uoZ1JSkjoXbTabqmf49jT2+1VXXQVAa2srY8eOJSkpiZSUFBwOB7qu89vf/hboTZKpqanous7evXv54he/iMViUft49+7dvPvuu9hsNhYuXKgaOKWlpWRlZTFmzBgcDgehUIjNmzfzt7/9DZvNxv3334/dbqe5uZmvfvWrWCwWHnnkESwWC++++y4vvPACKSkpPP3006SmptLV1UVRURHd3d1MmzaNHTt20NPTw1NPPUVjYyMej4fbbruNYDDIoUOHmDt3LsFgkM2bN5OcnMy+ffv4r//6L06dOsXWrVv55je/SUVFBRaLhYceegiXy8XWrVvp6enhZz/7GU6nk6SkJO677z4mTpzIW2+9RXV1NXv37uUnP/kJ3/ve93j22Wf54x//SGpqKk8//TSLFy/mxRdfBOD06dN9yl7YAAk3YHJyu91q8i0lJYWCggJ8vt6fLK+qqlJJZcGCBRw8eJAJEybgcrkoLCxULVGj7Jw5cwiFQtx8880cPXoUv9/PuHHjGD9+PH6/X5XLzMxk6tSpJCUlcfjwYWbMmEFzczNFRUVkZmaqlamqqiIjIwO73c4tt9zCiRMnVCti0qRJZGRkAL0nuBFT13X8fj8zZswAoKmpiaysLHVCVFVV0dbWhtVq5dprr6WtrY2kpCTOnDlDQUEBqampKub48ePVyWO325k5cybJyckkJSXhdrvVRScQCOByudTJ0dXVRUpKiuo1Wq1WdUIZB7HFYqG7u5s5c+aobW/09IyLtvHd0HuCJiUlqZ6Y3W6noKCgdwf/X+vfqHdJSQkWi0W1viwWC2PGjImImZeXR3t7u+qNhO//8Na1xWLB7/erbT169Gh0XSc5OVldYA2pqak4HA5Vz/T0dKxWK3a7nQ8++CAiZiAQ4NprrwVQ65GcnKzqbRwD6enpdHR0YLfb1ffabDYcDgfvv/9+xPdrmqa2p3GhTk5OVnUPv3gb2xNQF74JEyb027NIT09H13WmTJmCruuqhRxu9OjR1NXVqZhGQym80RG+/hfud4vFwuLFi9V6QO+kuM/nU/vM6ImHQqGIC7zRQCspKQFQCd5isfDZz3424rsdDgcdHR0qVrgLeyZ2u53Zs2dHbDtN00hJSVGNNoD58+erkRdd18nMzFTrEB7TaPRNnz4di8VCcnKy6kkY551xnbjuuus4d+6c2p5Ggy28jFFHXde5/vrrAZg8ebLqiRjb06in1Wqls7NTxTx37hxWqxVN0ygoKFDxLRYLRUVFKmZqair79+/HZrPxhS98IWJ7ZGZmMnr0aFwuF4FAQCWhQCCgYra0tGC327nmmmu4/fbbCYVCjBkzhurqarxeL5mZmaphFQgEmDVrFrm5ucyYMUMlf7vdzqJFi9S539HRwVVXXUV+fj5f/vKXARg7diyVlZXk5eWRkpKiGlJ79+6loKCA3Nxc5s6dSyAQ4MCBA+zbt48bb7xRrY+u66SlpZGXl8ett97KBx98wJw5c/j973/PjTfeiN1uJycnh7y8PPbs2aManCUlJbz//vvouk5VVVWfsseOHeNiov49p9raWk6cOKFa1M3NzbjdbqA3iYUP+9TW1tLU1ITH41Flx48fzxe+8AUee+wxuru7mThxIqNGjeLEiRPcdNNNqlx3dzcFBQVcffXVPP/88zQ1NTFq1ChGjRrVJ6bNZmPatGkqptErCY+p67qKmZaWhs/n46233mLUqFGkpqaqFoYR0+/3k52dzcsvv6xaaUeOHFEHrxHz+PHjhEIhnE4nLS0t7N69G5vNpoY1jeFLu91OTU0NmqYRCASora0lKSmJrq4udaF96aWXAHC5XPh8PkaNGsXZs2f505/+BPQmi48//hj4e4s2/OJRW1urWqPBYJCOjg6109PT02lpaUHXdYLBIJWVlRHDibqus2rVqoiYtbW1hEIhdfEMBAJ0dHRw/PjxiAt0ZmYmPp+P5ubmiOGvrq4u1q9fr05oY1jTSHi6rqsWq67r1NbW4nA48Pv9pKSk0N7ezsGDB4HeC3QgEFC9g/AhQWPo12AM9em6TldXl7oQWK1W/H4/b775phqqMoaJ169fDxBxwTT2kbGdgsEgBw4cAFD1zM3N5cyZM+pimJaWpoaoje1prOuZM2ciWoihUAi/36/+3UggmZmZNDU1qf3udrs5e/Ysuq7zgx/8IOJ8PHnyJMFgUPUKjHX2+/1q3+m6jtPppLW1lXfeeSeiTrqus3379oiYfr+fc+fOqe1mbCMgIsFomkZXVxdvv/020Ns4MtbPOBYMb775puo9GnGMbdbU1KQSqtvtpr6+ngMHDhAKhXC5XJw7dw7o7d2Ex/zrX/8aMbSn67r62+fzqe1ZUFDAyZMn2bt3L9A7hGc0hDZu3Bix7sFgkNraWlJSUrBarXR3dxMMBtX1JHwf7dmzRx1348eP58iRI/j9fjZu3KjOA+NiXl5eTmdnJ8FgUJ3/xnFls9nU8pqaGlauXEkwGKSzs5P29nZqa2tZt26dmsoIhULk5+dTXV3NJ598AvQmx5aWFnbs2IGu64wbN46jR49is9kIhUK8/vrravu3trbS1tbGypUrWbJkCW+88QadnZ3k5ORQXV3Nxx9/THJyMufPn6ejo4P33nuPl19+mVWrVvHzn/+ctrY2PB6PGjkKv36+9NJLTJw4kaysLE6fPq2uqUbZ1tZWfD4fkyZNUts8KytLdXb6E9UNEV1dXWzZsoWlS5eqYaFLlb3uuuvUiQO9F46qqiqeeeYZnE4n7e3tPPHEEyxdujSipQe9J0lVVRVOp5MvfOEL+P1+nnjiiT4xu7u7OXPmjIrp9/v5wx/+cNGYnZ2dpKSkqJg+n49rrrkmIqYxj/TUU0/hdDrVeHh6ejqzZs1SF2BjiKq7uxur1cqdd96J0+nk0KFDTJ8+ndtuu039u2H27NlYrVZ6enpoa2vDZrNx/fXXqxOvvb0dXddpbm7GYrHwla98BYATJ07gcDi45ppr+mzvgoIClUB6enpUT8gYgjJiQm/y+fKXv6wuTklJSWq4Ff4+jFRYWKj2szHPl5ubq7apUV+jle31etF1naNHj6JpGlOmTCE/Pz+iBe10OrHZbKoVDJCfn6+SjXFx6ejowGq1qhbf4cOHcTgcTJs2DZvNFhFz+vTpEcOfqampqmUePqdl9C6MoRhjWNEY7g1nnEx2u51QKITVaiU7O1v1dIx6Njc3Y7Va8Xq9QG+v2+FwMGXKFBXT2E6zZs3C6XSqY8bpdEa09o16tra2YrVa1X4/e/Ys0DvEagwxG+bMmYPT6VT1MXqixvY1tlF7ezt2u5077rgDgDNnzqiYxhCtYd68eRENFyNBh/e+jdjhMY2hykmTJqnerVH+H//xHyOuGbNmzVJDcOFzf0Y9jWHFuro6AGbMmKFa7obFixdHbM/MzMyImMb2NBLVuHHjAHjnnXfU8Rne4zT+axyjxueNfRQ+YtPe3s4Xv/hFtY8+/vhjNfdkHJ9Gz2n06NHs3LlTjUwcP35cXWuMuWhje6alpbF9+3YcDgeffPIJGRkZ3HLLLep6EX49Ky4uVj335ORkxo4dS3Z2Nvn5+dTW1qpy2dnZ3H333cDfh73vueceRo8ezWuvvRZx7BcXF0eUdblcLFu2jB/96Ee88sorAw6/Adx9990UFxdf9N/7m+e9lEsmp0AgwJYtW5g3bx433HCDWu5yuWhsbASgsbGRjIyMiLLGcIxR9s9//jM5OTkEg0EyMjKor6/H7XaTlZXFH/7wB1599VU1+X7s2DFcLpfqSXR3d+N2u0lJSeHVV1/F7/eTlJREfX09VquV9evX093dTUNDA+np6SqmkUiMmMnJyfj9fn73u9+puqenp/Pqq6+qckarZ/PmzXR3d9PR0UFaWhq1tbUcOnRIrZNxYhsb/bXXXqOjo0MdoEar0hjCAXjvvffU5415AOOEMWIaJ1coFOJ3v/sd0HvyJyUl8eGHHwKo8WpAtS6NC7LFYqG+vl6te/iwkRHTaBEmJydz4sQJ1esyYn744Yd0d3dHnDw+nw+/36+GEYzvDIVCNDQ0AKgkcfLkyT6Ng87OTkKhUMTcivFLw5mZmRE9gGAwyMsvvwygTswTJ06o9TPq9cEHH9Dd3a0aAD09PWquITMzM+ICFJ5YjGGjY8eOkZ6eHtHrCwaDar8b61RXV4fFYomop7G9jHU35ruOHDlCenp6xPY8cOBARMyOjo4+PRzju8P3u+Hjjz9W28oo+5e//EVtU0D18Nra2iI+GwqF6OnpUdvTWM+PP/5YnQOG6urqiCSUnJysjsXw7za+z5iDMf7t2LFjqqzx35deeiniWAqFQhE9rfCbCwKBAEePHlV/G9vOqLNR9vXXX4/Ynsac2YUxjX1w/PhxFVPXdQ4fPhyxv416GTcDGcOJxj4ylkHv+fTHP/6R3//+90Bvo7urq4s9e/aoWEZPpaqqiu985zucP38eu91Oa2urasyG97AsFgunT5/m3/7t31SvMj09nffee497770Xl8uljv2zZ88SDAbVEKEx9fIP//APbN68WTUEAoEADQ0NNDY2qoaWy+XC6/Xy1FNPcfXVV9Pd3Y3T6VQJLRgM0tXVRU5ODh6Ph4aGBkaPHq3O67S0NBoaGtToTHt7O263W50Dxr4IX2aUTUtLUzHDy17YQAo3YHLSdZ0dO3ZQUFDAkiVLIv6tuLiYyspKACorKykuLh6w7KlTpzh69CgVFRWqdTJ37lwmTZrE5z73OZYsWcJNN91EU1MTn3zyCQcPHqSwsJC33noLh8PB3LlzycrKYsmSJTgcDjVsV1tbS3FxMbm5uYRCIRYtWqRiGuPYRsyuri5yc3MJBAIkJSUxduxYPB6Pqu9NN91EVlYWHR0dTJ48mezsbEKhEFOnTmXs2LHMnz9fxTx48KA6cD0ejzqAjTkk4yYHq9WqhtV0XScrK4tQKISu6+Tk5JCVlcVXvvIVrFYrNTU12Gw2Ojs7cTqd6gYBq9Wq5h+SkpJUa8rYRy6XS8WcNm0ac+fOVRdmY/gQehOCcSE3GgKapvHoo49GzOUEAgEyMzMBVMvR6JGkpqaqE6izs1P1RgA1p2S1WvnGN77R51gyLnZGXYx5pSlTpkTcpWdMqkNvL2PcuHGkpqbi8Xj6XCAzMzPVCT569GgKCwtV6zi8nna7XX2H0eq02WysXr1a1c8QXk+n00lubi5paWlMmzZNxWhtbVUT5YCaP0xKSuLxxx/vU09jvxuNgoKCAjIzM9U2MFitVvUdxv5OS0vjO9/5TkS5np6eiJtkHA4H06dPJy8vr8+cl8PhUGVTUlJITk4mLS2Nhx56KKJcd3e3mh8FuPPOO7n22mvV8Hk4Y2jP2J7XX389Ho9H3RRjJJfu7m51ATJ6hdOnT8dms6kbCKA3YYcnKpvNxjXXXMPo0aNV79RYh+7ubjweT0SPzYhpzIPB33u3Rky73a4auf29eyA/P18NHbtcLrWPvv71r0fU07jYG3ELCwtJS0vj+9//fkQvU9M07rnnHrWdkpKSmD59OlOnTuWuu+4CehvuRlK7+eab1bltNDQmT55Md3e3mpt69913VSK02WwUFBTwmc98hj179nDy5EnOnz+P0+nk1KlTnDlzhldeeQVd1zlx4gTz58/nj3/8I7W1tZw6dQq/389nP/tZzpw5Q21tLW+99RY2m40JEyYwY8YM9u7dy9mzZ/nkk0+wWCy0trZy7tw5XnvtNaZOncqf//xnbr31Vvbu3auGw8+dO8e8efN44403AHj77bfVOVlcXNynrDFN1J8BH8I9fPgw69atU3M5APfeey8zZ86ktbWVsrIy6uvr8Xq9LFmyhE2bNlFQUEBNTQ2hUAiHw0FqaiobNmxgx44dnDx5kq6uLgKBAE6nE7fbTU1NDVarVU1kjxo1ijNnzqj5BGMIxu/3R8x3uFwuHA4HLS0t6oLrcDjIzs5W4/QG4+6onp6eiIOqv9uZ+7ud2uFwqHkKteH+r5scfgupsfzCTWpcxC68PT38OwxJSUkRQ4EXYySf8Bj93X470HddKDk5WZ0Yl2Jsz0vxeDw0NzdHVc/+biXvj3EjRHg9jTmBC/V3m3J/MjIyIu5WHKie/d1K3h+3201ra2tUMaOtZ25ubkTPeKCY0d5OnZOTQ319fUSMi+2LaOvpcrkiHt2Aiw/tDCamccee4WLHdrQx+7s9/WLb7bbbblM9JmN9pk2bFjGiAr03iLW0tLB///6I7zHmzsKT8BNPPMGuXbs4ceIE8PcblE6dOhURs6SkhClTpvDCCy+obfqNb3yD1NRUnn32WXU8pqWlsXr1anbt2qWug+np6UybNo0zZ85QU1Oj5tLvuusulixZom4l13WdxYsXM27cOMrLy6mvr8fv96shvjfffJPDhw+rW8lLS0u58847+fWvf83//u//4nA4+Na3vqVuJT9x4oSqj3FX7a9//Wv+9Kc/YbFYWLp0qbo5rT/yhgghhBCmI2+IEEIIYTqSnIQQQpiOJCchhBCmI8lJCCGE6UhyEkIIYTqSnIQQQpiOJCchhBCmI8lJCCGE6fx/efzlkt/U5K0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Performance.plot_pnlmoney(Performance.pnl_fifo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP6: 存储结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Account.save()\n",
    "Risk.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STEP7: 查看存储的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "account_info=QA.QA_fetch_account({'account_cookie':'JCSC_EXAMPLE'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: QUANTAXIS 1.0.47 has changed the init_assets ==> init_cash, please pay attention to this change if you using init_cash to initial an account class,                \n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "account=QA.QA_Account().from_message(account_info[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "< QA_Account JCSC_EXAMPLE>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
